library(tidyverse)
library(quanteda)
#devtools::install_github("quanteda/readtext")
library(readtext)
library(striprtf)
library(corpus)
library(quanteda.textplots)
library(readr)
library(topicmodels)
library(tidytext)
library(dplyr)
library(ggplot2)
library(tidyr)
library(text2vec)
library(tm)Blog Post 4
MekhalaKumar
Olympics2020
GenderandSports
BlogPost4
LDA Topic Modelling
LDA Topic Modelling
Initial steps
First a few more common words were removed from the document feature matrix so that the analysis is not cluttered by those words.
articles_dfm<-readRDS(file = "Data/News_DFM.rds")
articles_dfmDocument-feature matrix of: 1,157 documents, 22,370 features (99.21% sparse) and 3 docvars.
features
docs solitary two-day fixture great britain france 1900 olympics prospects
text1 1 1 1 1 1 1 1 9 3
text2 0 0 0 0 0 0 0 2 0
text3 0 0 0 2 2 0 0 3 0
text4 0 0 0 0 0 0 0 10 0
text5 0 0 0 0 0 0 0 3 0
text6 0 0 0 2 1 0 0 2 0
features
docs cricket's
text1 2
text2 0
text3 0
text4 0
text5 0
text6 0
[ reached max_ndoc ... 1,151 more documents, reached max_nfeat ... 22,360 more features ]#textplot_wordcloud(articles_dfm, min_count = 50, random_order = FALSE)
articles_dfm <- dfm_remove(articles_dfm, c("said","also","says","can","just"), verbose = TRUE)removed 5 features#textplot_wordcloud(articles_dfm, min_count = 50, random_order = FALSE)Semantic Network
For the semantic network, I limited the document feature matrix to terms that appeared a least 15 times and in 25% of the documents. This consisted of 48 terms which I plotted.
Unsurprisingly, this shows that most of the articles discuss India in the Olympics (as Indian newspaper articles were used). One major theme that can be observed is the discussion of the hockey team, the men’s team had placed third in over four decades hence marking history and was led by the captain Manpreet Singh. Other significant terms include medals and medal colours perhaps pertaining to victories by other Indian athletes; which may be more clearly observed through a topic model.
dfm_refined <- dfm_trim(articles_dfm, min_termfreq = 15)
dfm_refined <- dfm_trim(dfm_refined, min_docfreq = .25, docfreq_type = "prop")
fcm<- fcm(dfm_refined)
dim(fcm)[1] 48 48top_features <- names(topfeatures(fcm, 48))
fcm_refined <- fcm_select(fcm, pattern = top_features, selection = "keep")
dim(fcm_refined)[1] 48 48size <- log(colSums(fcm_refined))
textplot_network(fcm_refined, vertex_size = size / max(size) * 3)
Topic Modelling
I used the topicmodels package to run a Latent Dirichlet Allocation topic model.
##Preparatory steps
To run the model, the data had to be in the form of a document term matrix. First the document feature matrix was converted into a one-token-per-document-per-row table and then this table was converted into a document term matrix.
articles_tidy<-tidy(articles_dfm)
articles_tidy# A tibble: 201,774 × 3
document term count
<chr> <chr> <dbl>
1 text1 solitary 1
2 text214 solitary 1
3 text245 solitary 1
4 text629 solitary 1
5 text639 solitary 1
6 text797 solitary 1
7 text1099 solitary 1
8 text1 two-day 1
9 text311 two-day 1
10 text368 two-day 1
# … with 201,764 more rowsnews_dtm <- articles_tidy %>%
cast_dtm(document, term, count)
news_dtm<<DocumentTermMatrix (documents: 1157, terms: 22365)>>
Non-/sparse entries: 201774/25674531
Sparsity : 99%
Maximal term length: 84
Weighting : term frequency (tf)Word topic probabilities
3 topics
There was not much valuable information being provided by keeping only three topics.
The first topic seemed to be about Neeraj Chopra winning the gold medal in javelin throw and PV Sindhu winning the bronze medal in badminton and the second topic seemed to focus on hockey. The third topic just had common terms pertaining to Olympics.
Hence, I ran a search_k function in order to find the optimal number of topics to use to derive a meaningful analysis.
news_lda <- LDA(news_dtm, k = 3, control = list(seed = 2345))
news_ldaA LDA_VEM topic model with 3 topics.#extracting per-topic-per-word probabilities
news_topics <- tidy(news_lda, matrix = "beta")
news_topics# A tibble: 67,095 × 3
topic term beta
<int> <chr> <dbl>
1 1 solitary 5.64e- 5
2 2 solitary 1.41e-10
3 3 solitary 1.26e- 5
4 1 two-day 5.31e- 5
5 2 two-day 1.65e- 5
6 3 two-day 8.99e- 6
7 1 fixture 4.24e- 5
8 2 fixture 1.49e-32
9 3 fixture 3.13e- 5
10 1 great 2.53e- 3
# … with 67,085 more rows#Finding the top 10 terms
news_top_10 <- news_topics %>%
group_by(topic) %>%
slice_max(beta, n = 10) %>%
ungroup() %>%
arrange(topic, -beta)
news_top_10%>%
mutate(term = reorder_within(term, beta, topic)) %>%
ggplot(aes(beta, term, fill = factor(topic))) +
geom_col(show.legend = FALSE) +
facet_wrap(~ topic, scales = "free") +
scale_y_reordered()
Choosing K
Based on the semantic coherence, I selected K as 25.
library(stm)Warning: package 'stm' was built under R version 4.2.2stm v1.3.6 successfully loaded. See ?stm for help.
Papers, resources, and other materials at structuraltopicmodel.comdifferentKs <- searchK(articles_dfm,
K = c(5,10,15,25,50),
N = 250,
data = articles_tidy,
max.em.its = 1000,
init.type = "Spectral")Beginning Spectral Initialization
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Topic 1: olympics, sindhu, indian, world, tokyo
Topic 2: medal, olympics, gold, world, tokyo
Topic 3: medal, tokyo, olympics, chanu, mirabai
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Topic 10: olympics, wrote, shared, tokyo, twitter
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Completing Iteration 30 (approx. per word bound = -7.486, relative change = 6.800e-05)
Topic 1: medal, lovlina, olympics, world, bronze
Topic 2: gold, medal, neeraj, chopra, olympics
Topic 3: medal, chanu, mirabai, silver, tokyo
Topic 4: medal, gold, olympics, bronze, chopra
Topic 5: hockey, team, india, indian, match
Topic 6: olympics, athletes, women, world, biles
Topic 7: world, round, olympics, men's, team
Topic 8: hockey, team, sports, players, indian
Topic 9: sindhu, medal, pv, olympics, indian
Topic 10: olympics, wrote, shared, tokyo, twitter
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Completing Iteration 35 (approx. per word bound = -7.483, relative change = 5.545e-05)
Topic 1: medal, lovlina, olympics, world, bronze
Topic 2: gold, medal, neeraj, chopra, olympics
Topic 3: medal, chanu, mirabai, silver, tokyo
Topic 4: medal, gold, olympics, chopra, bronze
Topic 5: hockey, team, india, indian, match
Topic 6: olympics, athletes, women, world, biles
Topic 7: world, round, olympics, men's, team
Topic 8: hockey, team, sports, players, indian
Topic 9: sindhu, medal, pv, olympics, indian
Topic 10: olympics, wrote, twitter, shared, tokyo
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Completing Iteration 40 (approx. per word bound = -7.481, relative change = 6.633e-05)
Topic 1: medal, lovlina, olympics, world, bronze
Topic 2: gold, medal, neeraj, chopra, olympics
Topic 3: medal, chanu, mirabai, silver, tokyo
Topic 4: medal, gold, olympics, chopra, tokyo
Topic 5: hockey, team, india, indian, match
Topic 6: olympics, athletes, women, world, biles
Topic 7: world, round, olympics, men's, team
Topic 8: hockey, team, sports, indian, players
Topic 9: sindhu, medal, pv, olympics, tokyo
Topic 10: olympics, wrote, twitter, shared, tokyo
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Completing Iteration 45 (approx. per word bound = -7.480, relative change = 2.398e-05)
Topic 1: medal, lovlina, olympics, world, bronze
Topic 2: gold, medal, neeraj, chopra, olympics
Topic 3: medal, chanu, mirabai, silver, tokyo
Topic 4: medal, gold, olympics, chopra, tokyo
Topic 5: hockey, team, india, indian, match
Topic 6: olympics, athletes, women, world, biles
Topic 7: world, round, olympics, men's, team
Topic 8: hockey, team, sports, indian, players
Topic 9: sindhu, medal, pv, olympics, tokyo
Topic 10: olympics, wrote, twitter, medal, indian
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Completing Iteration 50 (approx. per word bound = -7.479, relative change = 2.208e-05)
Topic 1: medal, lovlina, olympics, world, bronze
Topic 2: gold, medal, neeraj, chopra, olympics
Topic 3: medal, chanu, mirabai, silver, tokyo
Topic 4: medal, gold, olympics, tokyo, chopra
Topic 5: hockey, team, india, indian, match
Topic 6: olympics, athletes, women, world, biles
Topic 7: world, round, olympics, men's, team
Topic 8: hockey, team, sports, indian, players
Topic 9: sindhu, medal, pv, olympics, tokyo
Topic 10: olympics, wrote, medal, twitter, indian
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Completing Iteration 55 (approx. per word bound = -7.478, relative change = 1.969e-05)
Topic 1: medal, lovlina, olympics, world, bronze
Topic 2: gold, medal, neeraj, chopra, olympics
Topic 3: medal, chanu, mirabai, silver, tokyo
Topic 4: medal, gold, olympics, tokyo, chopra
Topic 5: hockey, team, india, indian, match
Topic 6: olympics, athletes, women, world, biles
Topic 7: world, round, olympics, men's, team
Topic 8: hockey, team, sports, indian, players
Topic 9: sindhu, medal, pv, olympics, tokyo
Topic 10: olympics, wrote, medal, indian, twitter
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Completing Iteration 60 (approx. per word bound = -7.477, relative change = 2.464e-05)
Topic 1: medal, lovlina, olympics, world, bronze
Topic 2: gold, medal, neeraj, chopra, olympics
Topic 3: medal, chanu, mirabai, silver, tokyo
Topic 4: medal, gold, olympics, tokyo, chopra
Topic 5: hockey, team, india, match, indian
Topic 6: olympics, athletes, women, world, biles
Topic 7: world, round, olympics, men's, team
Topic 8: hockey, team, sports, indian, players
Topic 9: sindhu, medal, pv, olympics, tokyo
Topic 10: olympics, wrote, medal, indian, twitter
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Completing Iteration 61 (approx. per word bound = -7.477, relative change = 3.077e-05)
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Completing Iteration 65 (approx. per word bound = -7.476, relative change = 1.447e-05)
Topic 1: medal, lovlina, olympics, world, bronze
Topic 2: gold, medal, neeraj, chopra, throw
Topic 3: medal, chanu, mirabai, silver, tokyo
Topic 4: medal, gold, olympics, tokyo, chopra
Topic 5: hockey, india, team, match, indian
Topic 6: olympics, athletes, women, world, biles
Topic 7: world, round, olympics, men's, team
Topic 8: hockey, team, sports, indian, players
Topic 9: sindhu, medal, pv, olympics, tokyo
Topic 10: olympics, wrote, indian, medal, twitter
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Completing Iteration 66 (approx. per word bound = -7.476, relative change = 1.182e-05)
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Completing Iteration 70 (approx. per word bound = -7.476, relative change = 1.751e-05)
Topic 1: medal, lovlina, olympics, world, bronze
Topic 2: gold, medal, neeraj, chopra, throw
Topic 3: medal, chanu, mirabai, silver, tokyo
Topic 4: medal, gold, olympics, tokyo, rs
Topic 5: india, hockey, team, match, indian
Topic 6: olympics, athletes, women, world, biles
Topic 7: world, round, olympics, men's, team
Topic 8: hockey, team, sports, indian, players
Topic 9: sindhu, medal, pv, olympics, tokyo
Topic 10: olympics, wrote, indian, medal, twitter
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Completing Iteration 71 (approx. per word bound = -7.476, relative change = 2.027e-05)
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Completing Iteration 75 (approx. per word bound = -7.475, relative change = 2.833e-05)
Topic 1: medal, lovlina, olympics, world, bronze
Topic 2: gold, medal, neeraj, chopra, throw
Topic 3: medal, chanu, mirabai, silver, tokyo
Topic 4: medal, gold, olympics, tokyo, rs
Topic 5: india, hockey, team, match, indian
Topic 6: olympics, athletes, women, world, biles
Topic 7: world, round, olympics, men's, team
Topic 8: hockey, team, sports, indian, players
Topic 9: sindhu, medal, pv, olympics, tokyo
Topic 10: olympics, wrote, indian, medal, twitter
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Completing Iteration 76 (approx. per word bound = -7.475, relative change = 2.833e-05)
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Completing Iteration 79 (approx. per word bound = -7.474, relative change = 2.439e-05)
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Completing Iteration 80 (approx. per word bound = -7.474, relative change = 2.158e-05)
Topic 1: medal, lovlina, olympics, world, bronze
Topic 2: gold, medal, neeraj, chopra, throw
Topic 3: medal, chanu, mirabai, silver, tokyo
Topic 4: medal, gold, olympics, tokyo, rs
Topic 5: india, hockey, team, match, indian
Topic 6: olympics, athletes, women, world, biles
Topic 7: world, round, olympics, men's, team
Topic 8: hockey, team, sports, indian, players
Topic 9: sindhu, medal, pv, olympics, tokyo
Topic 10: olympics, wrote, indian, medal, india
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Completing Iteration 81 (approx. per word bound = -7.474, relative change = 1.807e-05)
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Completing Iteration 82 (approx. per word bound = -7.474, relative change = 1.307e-05)
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Completed E-Step (0 seconds).
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Model Converged
Beginning Spectral Initialization
Calculating the gram matrix...
Using only 10000 most frequent terms during initialization...
Finding anchor words...
...............
Recovering initialization...
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Initialization complete.
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Completed E-Step (1 seconds).
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Completing Iteration 1 (approx. per word bound = -8.217)
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Completing Iteration 2 (approx. per word bound = -7.428, relative change = 9.595e-02)
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Completing Iteration 3 (approx. per word bound = -7.384, relative change = 6.005e-03)
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Completing Iteration 4 (approx. per word bound = -7.369, relative change = 1.968e-03)
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Completing Iteration 5 (approx. per word bound = -7.364, relative change = 7.842e-04)
Topic 1: athletes, olympics, indian, tokyo, games
Topic 2: gold, medal, neeraj, chopra, throw
Topic 3: medal, chanu, mirabai, silver, olympics
Topic 4: gold, medal, chopra, olympics, neeraj
Topic 5: hockey, india, team, indian, medal
Topic 6: sports, olympics, hockey, women, team
Topic 7: men's, team, world, round, olympics
Topic 8: hockey, team, indian, india, olympics
Topic 9: sindhu, medal, olympics, pv, indian
Topic 10: olympics, tokyo, video, match, us
Topic 11: medal, world, lovlina, olympic, bronze
Topic 12: olympics, biles, one, olympic, like
Topic 13: athletes, olympic, olympics, world, like
Topic 14: athletes, tokyo, games, olympics, world
Topic 15: medal, olympics, tokyo, bronze, games
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Completing Iteration 6 (approx. per word bound = -7.361, relative change = 3.885e-04)
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Completing Iteration 7 (approx. per word bound = -7.359, relative change = 2.563e-04)
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Completing Iteration 8 (approx. per word bound = -7.357, relative change = 1.920e-04)
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Completing Iteration 9 (approx. per word bound = -7.356, relative change = 1.653e-04)
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Completing Iteration 10 (approx. per word bound = -7.355, relative change = 1.324e-04)
Topic 1: athletes, olympics, indian, tokyo, games
Topic 2: gold, medal, neeraj, chopra, throw
Topic 3: medal, chanu, mirabai, silver, olympics
Topic 4: gold, medal, olympics, chopra, neeraj
Topic 5: hockey, india, team, indian, match
Topic 6: sports, hockey, women, olympics, team
Topic 7: men's, team, world, round, olympics
Topic 8: hockey, team, indian, india, olympics
Topic 9: sindhu, medal, olympics, pv, indian
Topic 10: olympics, tokyo, video, match, shared
Topic 11: medal, world, lovlina, olympic, bronze
Topic 12: olympics, biles, one, house, olympic
Topic 13: athletes, olympic, olympics, world, like
Topic 14: athletes, tokyo, games, olympics, world
Topic 15: medal, olympics, tokyo, bronze, medals
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Completed E-Step (0 seconds).
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Completing Iteration 11 (approx. per word bound = -7.354, relative change = 1.245e-04)
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Completing Iteration 12 (approx. per word bound = -7.353, relative change = 1.083e-04)
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Completing Iteration 13 (approx. per word bound = -7.353, relative change = 9.888e-05)
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Completing Iteration 14 (approx. per word bound = -7.352, relative change = 9.632e-05)
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Completing Iteration 15 (approx. per word bound = -7.351, relative change = 8.668e-05)
Topic 1: athletes, olympics, indian, tokyo, games
Topic 2: gold, neeraj, medal, chopra, throw
Topic 3: chanu, medal, mirabai, silver, olympics
Topic 4: medal, gold, olympics, chopra, indian
Topic 5: hockey, india, team, match, indian
Topic 6: sports, hockey, women, olympics, team
Topic 7: men's, world, team, round, olympics
Topic 8: hockey, team, indian, india, women's
Topic 9: sindhu, medal, olympics, pv, indian
Topic 10: olympics, tokyo, video, match, shared
Topic 11: medal, world, lovlina, olympic, bronze
Topic 12: house, olympics, biles, opposition, one
Topic 13: athletes, olympic, world, olympics, like
Topic 14: athletes, tokyo, games, olympics, olympic
Topic 15: medal, olympics, tokyo, medals, games
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Completing Iteration 16 (approx. per word bound = -7.351, relative change = 8.250e-05)
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Completing Iteration 17 (approx. per word bound = -7.350, relative change = 7.569e-05)
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Completing Iteration 18 (approx. per word bound = -7.350, relative change = 6.628e-05)
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Completing Iteration 19 (approx. per word bound = -7.349, relative change = 6.085e-05)
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Completing Iteration 20 (approx. per word bound = -7.349, relative change = 6.122e-05)
Topic 1: athletes, olympics, indian, tokyo, games
Topic 2: gold, neeraj, medal, chopra, throw
Topic 3: chanu, medal, mirabai, silver, olympics
Topic 4: medal, gold, olympics, chopra, indian
Topic 5: india, hockey, team, match, indian
Topic 6: sports, hockey, women, olympics, team
Topic 7: men's, world, team, round, olympics
Topic 8: hockey, team, indian, india, women's
Topic 9: sindhu, medal, olympics, pv, bronze
Topic 10: olympics, tokyo, video, match, shared
Topic 11: medal, world, lovlina, olympic, bronze
Topic 12: house, opposition, olympics, biles, one
Topic 13: athletes, olympic, world, olympics, like
Topic 14: athletes, tokyo, games, olympics, world
Topic 15: medal, olympics, tokyo, games, medals
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Completing Iteration 21 (approx. per word bound = -7.348, relative change = 6.345e-05)
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Completing Iteration 22 (approx. per word bound = -7.348, relative change = 6.726e-05)
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Completing Iteration 23 (approx. per word bound = -7.347, relative change = 6.480e-05)
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Completing Iteration 24 (approx. per word bound = -7.347, relative change = 4.608e-05)
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Completing Iteration 25 (approx. per word bound = -7.347, relative change = 3.420e-05)
Topic 1: athletes, olympics, indian, tokyo, games
Topic 2: gold, neeraj, medal, chopra, throw
Topic 3: chanu, medal, mirabai, silver, olympics
Topic 4: medal, gold, olympics, indian, chopra
Topic 5: india, hockey, team, match, indian
Topic 6: sports, hockey, women, olympics, team
Topic 7: men's, world, team, round, olympics
Topic 8: hockey, team, indian, india, women's
Topic 9: sindhu, medal, olympics, pv, bronze
Topic 10: olympics, video, tokyo, shared, match
Topic 11: medal, world, lovlina, olympic, bronze
Topic 12: house, opposition, biles, olympics, one
Topic 13: athletes, olympic, world, olympics, like
Topic 14: athletes, tokyo, olympics, games, world
Topic 15: medal, olympics, tokyo, games, medals
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Completed E-Step (0 seconds).
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Completing Iteration 26 (approx. per word bound = -7.347, relative change = 2.739e-05)
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Completing Iteration 27 (approx. per word bound = -7.346, relative change = 2.568e-05)
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Completing Iteration 28 (approx. per word bound = -7.346, relative change = 2.951e-05)
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Completing Iteration 29 (approx. per word bound = -7.346, relative change = 2.922e-05)
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Completing Iteration 30 (approx. per word bound = -7.346, relative change = 3.314e-05)
Topic 1: athletes, olympics, indian, tokyo, games
Topic 2: gold, neeraj, medal, chopra, throw
Topic 3: chanu, medal, mirabai, silver, olympics
Topic 4: medal, gold, olympics, indian, win
Topic 5: india, hockey, team, match, indian
Topic 6: sports, hockey, women, olympics, team
Topic 7: men's, world, team, round, olympics
Topic 8: hockey, team, indian, india, women's
Topic 9: sindhu, medal, olympics, pv, bronze
Topic 10: olympics, video, tokyo, shared, match
Topic 11: medal, world, lovlina, olympic, bronze
Topic 12: house, opposition, biles, olympics, one
Topic 13: athletes, olympic, world, olympics, like
Topic 14: athletes, tokyo, olympics, games, world
Topic 15: medal, olympics, tokyo, games, medals
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Completed E-Step (0 seconds).
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Completing Iteration 31 (approx. per word bound = -7.346, relative change = 2.904e-05)
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Completing Iteration 32 (approx. per word bound = -7.345, relative change = 2.415e-05)
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Completing Iteration 33 (approx. per word bound = -7.345, relative change = 1.887e-05)
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Completing Iteration 34 (approx. per word bound = -7.345, relative change = 1.851e-05)
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Completing Iteration 35 (approx. per word bound = -7.345, relative change = 1.889e-05)
Topic 1: athletes, olympics, indian, tokyo, games
Topic 2: gold, neeraj, medal, chopra, throw
Topic 3: chanu, medal, mirabai, silver, olympics
Topic 4: medal, gold, olympics, indian, win
Topic 5: india, hockey, team, match, indian
Topic 6: sports, hockey, women, olympics, team
Topic 7: men's, world, team, round, olympics
Topic 8: hockey, team, indian, india, women's
Topic 9: sindhu, medal, olympics, pv, bronze
Topic 10: olympics, video, tokyo, shared, match
Topic 11: medal, world, lovlina, olympic, bronze
Topic 12: house, opposition, biles, olympics, one
Topic 13: athletes, olympic, world, olympics, like
Topic 14: athletes, tokyo, olympics, games, world
Topic 15: medal, olympics, tokyo, games, medals
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Completed E-Step (0 seconds).
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Completing Iteration 36 (approx. per word bound = -7.345, relative change = 2.256e-05)
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Completing Iteration 37 (approx. per word bound = -7.345, relative change = 2.639e-05)
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Completing Iteration 38 (approx. per word bound = -7.344, relative change = 2.164e-05)
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Completing Iteration 39 (approx. per word bound = -7.344, relative change = 2.014e-05)
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Completing Iteration 40 (approx. per word bound = -7.344, relative change = 1.849e-05)
Topic 1: athletes, olympics, indian, tokyo, games
Topic 2: gold, neeraj, medal, chopra, throw
Topic 3: chanu, medal, mirabai, silver, olympics
Topic 4: medal, gold, olympics, indian, win
Topic 5: india, hockey, team, match, indian
Topic 6: sports, hockey, women, olympics, team
Topic 7: men's, world, team, round, olympics
Topic 8: hockey, team, indian, india, women's
Topic 9: sindhu, medal, olympics, pv, bronze
Topic 10: olympics, video, tokyo, malik, shared
Topic 11: medal, world, lovlina, olympic, bronze
Topic 12: house, opposition, olympics, one, biles
Topic 13: athletes, olympic, world, olympics, like
Topic 14: athletes, tokyo, olympics, games, world
Topic 15: medal, olympics, tokyo, games, medals
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Completed E-Step (0 seconds).
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Completing Iteration 41 (approx. per word bound = -7.344, relative change = 1.637e-05)
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Completing Iteration 42 (approx. per word bound = -7.344, relative change = 1.549e-05)
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Completing Iteration 43 (approx. per word bound = -7.344, relative change = 1.558e-05)
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Completing Iteration 44 (approx. per word bound = -7.344, relative change = 1.600e-05)
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Completing Iteration 45 (approx. per word bound = -7.344, relative change = 1.642e-05)
Topic 1: athletes, indian, olympics, tokyo, games
Topic 2: gold, neeraj, medal, chopra, throw
Topic 3: chanu, medal, mirabai, silver, olympics
Topic 4: medal, gold, olympics, indian, win
Topic 5: india, hockey, team, match, indian
Topic 6: sports, hockey, women, olympics, team
Topic 7: men's, world, team, round, olympics
Topic 8: hockey, team, indian, india, women's
Topic 9: sindhu, medal, olympics, pv, bronze
Topic 10: olympics, video, tokyo, malik, shared
Topic 11: medal, world, lovlina, olympic, bronze
Topic 12: house, opposition, one, olympics, biles
Topic 13: athletes, olympic, world, olympics, like
Topic 14: athletes, tokyo, olympics, games, world
Topic 15: medal, olympics, tokyo, games, gold
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Completed E-Step (0 seconds).
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Completing Iteration 46 (approx. per word bound = -7.343, relative change = 1.535e-05)
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Completing Iteration 47 (approx. per word bound = -7.343, relative change = 1.659e-05)
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Completing Iteration 48 (approx. per word bound = -7.343, relative change = 1.896e-05)
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Completing Iteration 49 (approx. per word bound = -7.343, relative change = 2.162e-05)
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Completed E-Step (0 seconds).
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Completing Iteration 50 (approx. per word bound = -7.343, relative change = 2.326e-05)
Topic 1: athletes, indian, olympics, tokyo, games
Topic 2: gold, neeraj, medal, chopra, throw
Topic 3: chanu, medal, mirabai, silver, olympics
Topic 4: medal, olympics, gold, indian, win
Topic 5: india, hockey, team, match, indian
Topic 6: sports, hockey, women, olympics, team
Topic 7: men's, world, team, round, olympics
Topic 8: hockey, team, indian, india, women's
Topic 9: sindhu, medal, olympics, pv, bronze
Topic 10: olympics, video, tokyo, malik, shared
Topic 11: medal, world, lovlina, olympic, bronze
Topic 12: house, opposition, one, olympics, biles
Topic 13: athletes, olympic, world, olympics, like
Topic 14: athletes, tokyo, olympics, games, world
Topic 15: medal, olympics, tokyo, games, gold
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Completed E-Step (0 seconds).
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Completing Iteration 51 (approx. per word bound = -7.343, relative change = 2.383e-05)
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Completing Iteration 52 (approx. per word bound = -7.343, relative change = 2.194e-05)
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Completing Iteration 53 (approx. per word bound = -7.342, relative change = 2.252e-05)
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Completing Iteration 54 (approx. per word bound = -7.342, relative change = 4.207e-05)
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Completed E-Step (0 seconds).
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Completing Iteration 55 (approx. per word bound = -7.342, relative change = 5.729e-05)
Topic 1: athletes, indian, olympics, tokyo, games
Topic 2: gold, neeraj, medal, chopra, throw
Topic 3: chanu, medal, mirabai, silver, olympics
Topic 4: medal, olympics, gold, indian, win
Topic 5: india, hockey, team, match, indian
Topic 6: sports, hockey, women, olympics, team
Topic 7: men's, world, team, round, olympics
Topic 8: hockey, team, indian, india, women's
Topic 9: sindhu, medal, olympics, pv, bronze
Topic 10: olympics, video, tokyo, malik, shared
Topic 11: medal, world, lovlina, olympic, bronze
Topic 12: house, opposition, one, olympics, biles
Topic 13: athletes, olympic, world, olympics, like
Topic 14: athletes, tokyo, olympics, games, world
Topic 15: medal, olympics, tokyo, gold, games
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Completed E-Step (0 seconds).
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Completing Iteration 56 (approx. per word bound = -7.341, relative change = 3.187e-05)
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Completing Iteration 57 (approx. per word bound = -7.341, relative change = 2.440e-05)
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Completing Iteration 58 (approx. per word bound = -7.341, relative change = 2.196e-05)
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Completing Iteration 59 (approx. per word bound = -7.341, relative change = 1.616e-05)
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Completed E-Step (0 seconds).
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Completing Iteration 60 (approx. per word bound = -7.341, relative change = 1.466e-05)
Topic 1: athletes, indian, olympics, tokyo, games
Topic 2: gold, neeraj, medal, chopra, throw
Topic 3: chanu, medal, mirabai, silver, olympics
Topic 4: medal, olympics, gold, indian, win
Topic 5: india, hockey, team, match, indian
Topic 6: sports, hockey, women, olympics, team
Topic 7: men's, world, round, team, olympics
Topic 8: hockey, team, indian, india, women's
Topic 9: sindhu, medal, olympics, pv, bronze
Topic 10: olympics, video, tokyo, malik, shared
Topic 11: medal, world, lovlina, olympic, bronze
Topic 12: house, opposition, one, olympics, biles
Topic 13: athletes, olympic, world, olympics, like
Topic 14: athletes, tokyo, olympics, games, world
Topic 15: medal, olympics, tokyo, gold, games
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Completed E-Step (0 seconds).
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Completing Iteration 61 (approx. per word bound = -7.341, relative change = 1.490e-05)
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Completing Iteration 62 (approx. per word bound = -7.341, relative change = 1.499e-05)
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Completing Iteration 63 (approx. per word bound = -7.341, relative change = 1.607e-05)
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Completing Iteration 64 (approx. per word bound = -7.340, relative change = 2.088e-05)
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Completed E-Step (0 seconds).
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Completing Iteration 65 (approx. per word bound = -7.340, relative change = 2.406e-05)
Topic 1: athletes, indian, olympics, tokyo, games
Topic 2: gold, neeraj, medal, chopra, throw
Topic 3: chanu, medal, mirabai, silver, olympics
Topic 4: medal, olympics, gold, indian, win
Topic 5: india, hockey, team, match, indian
Topic 6: sports, hockey, olympics, women, team
Topic 7: men's, world, round, team, olympics
Topic 8: hockey, team, indian, india, women's
Topic 9: sindhu, medal, pv, olympics, bronze
Topic 10: olympics, video, tokyo, malik, shared
Topic 11: medal, world, lovlina, olympic, bronze
Topic 12: house, opposition, one, olympics, minister
Topic 13: athletes, olympic, world, olympics, like
Topic 14: athletes, tokyo, olympics, games, world
Topic 15: medal, olympics, tokyo, gold, games
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Completed E-Step (0 seconds).
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Completing Iteration 66 (approx. per word bound = -7.340, relative change = 2.052e-05)
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Completing Iteration 67 (approx. per word bound = -7.340, relative change = 1.847e-05)
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Completing Iteration 68 (approx. per word bound = -7.340, relative change = 1.242e-05)
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Completed E-Step (0 seconds).
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Model Converged
Beginning Spectral Initialization
Calculating the gram matrix...
Using only 10000 most frequent terms during initialization...
Finding anchor words...
.........................
Recovering initialization...
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Initialization complete.
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Completed E-Step (0 seconds).
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Completing Iteration 1 (approx. per word bound = -8.112)
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Completing Iteration 2 (approx. per word bound = -7.251, relative change = 1.061e-01)
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Completing Iteration 3 (approx. per word bound = -7.177, relative change = 1.025e-02)
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Completing Iteration 4 (approx. per word bound = -7.157, relative change = 2.763e-03)
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Completed E-Step (0 seconds).
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Completing Iteration 5 (approx. per word bound = -7.149, relative change = 1.148e-03)
Topic 1: manika, indian, game, sharath, round
Topic 2: throw, neeraj, gold, chopra, medal
Topic 3: mirabai, medal, chanu, india, tokyo
Topic 4: gold, neeraj, medal, chopra, olympics
Topic 5: hockey, india, team, medal, match
Topic 6: women, hockey, olympics, men, team
Topic 7: men's, team, round, women's, world
Topic 8: hockey, team, indian, women's, players
Topic 9: sindhu, medal, pv, olympics, bronze
Topic 10: olympics, video, shared, tokyo, match
Topic 11: lovlina, medal, borgohain, boxing, boxer
Topic 12: biles, olympics, like, one, world
Topic 13: olympic, sharma, hai, back, years
Topic 14: tokyo, games, world, osaka, first
Topic 15: olympics, athletes, tokyo, medal, games
Topic 16: sports, world, one, hockey, like
Topic 17: chand, dhyan, award, ratna, khel
Topic 18: medal, dahiya, world, bronze, gold
Topic 19: india, team, penalty, indian, goal
Topic 20: singh, hockey, team, medal, olympics
Topic 21: chanu, mirabai, medal, olympics, minister
Topic 22: sports, games, athletes, olympics, training
Topic 23: team, hockey, indian, india, women's
Topic 24: gold, medal, aditi, olympics, ashok
Topic 25: rs, crore, cash, chopra, announced
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Completed E-Step (1 seconds).
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Completing Iteration 6 (approx. per word bound = -7.144, relative change = 6.985e-04)
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Completing Iteration 7 (approx. per word bound = -7.140, relative change = 5.381e-04)
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Completed E-Step (1 seconds).
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Completing Iteration 8 (approx. per word bound = -7.137, relative change = 4.401e-04)
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Completed E-Step (1 seconds).
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Completing Iteration 9 (approx. per word bound = -7.134, relative change = 3.378e-04)
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Completed E-Step (1 seconds).
Completed M-Step.
Completing Iteration 10 (approx. per word bound = -7.133, relative change = 2.472e-04)
Topic 1: manika, indian, game, singles, sharath
Topic 2: throw, gold, neeraj, chopra, medal
Topic 3: chanu, mirabai, medal, silver, india
Topic 4: gold, medal, neeraj, chopra, olympics
Topic 5: hockey, india, team, medal, olympics
Topic 6: women, hockey, olympics, men, team
Topic 7: men's, team, round, women's, world
Topic 8: hockey, team, indian, players, women's
Topic 9: sindhu, pv, medal, olympics, bronze
Topic 10: olympics, video, shared, tokyo, post
Topic 11: lovlina, medal, borgohain, boxing, boxer
Topic 12: biles, olympics, one, like, world
Topic 13: olympic, sharma, hai, back, years
Topic 14: tokyo, games, world, osaka, olympics
Topic 15: olympics, athletes, tokyo, olympic, games
Topic 16: sports, world, one, hockey, like
Topic 17: chand, dhyan, award, ratna, khel
Topic 18: medal, dahiya, world, wrestling, bronze
Topic 19: india, team, penalty, goal, indian
Topic 20: singh, hockey, team, medal, olympics
Topic 21: chanu, mirabai, medal, minister, sports
Topic 22: sports, games, athletes, olympics, training
Topic 23: team, hockey, indian, india, women's
Topic 24: gold, aditi, medal, olympics, ashok
Topic 25: rs, cash, crore, announced, lakh
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Completed E-Step (1 seconds).
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Completing Iteration 11 (approx. per word bound = -7.131, relative change = 1.938e-04)
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Completed E-Step (1 seconds).
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Completing Iteration 12 (approx. per word bound = -7.130, relative change = 1.566e-04)
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Completed E-Step (1 seconds).
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Completing Iteration 13 (approx. per word bound = -7.129, relative change = 1.477e-04)
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Completed E-Step (1 seconds).
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Completing Iteration 14 (approx. per word bound = -7.128, relative change = 1.607e-04)
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Completed E-Step (1 seconds).
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Completing Iteration 15 (approx. per word bound = -7.126, relative change = 1.910e-04)
Topic 1: manika, indian, singles, round, game
Topic 2: throw, neeraj, gold, chopra, medal
Topic 3: chanu, mirabai, medal, silver, india
Topic 4: medal, gold, neeraj, chopra, win
Topic 5: hockey, india, team, medal, olympics
Topic 6: women, hockey, olympics, men, first
Topic 7: men's, team, round, women's, world
Topic 8: hockey, team, indian, players, women's
Topic 9: sindhu, pv, medal, olympics, bronze
Topic 10: olympics, video, shared, tokyo, post
Topic 11: lovlina, medal, borgohain, boxing, boxer
Topic 12: biles, olympics, one, like, world
Topic 13: olympic, sharma, hai, back, years
Topic 14: tokyo, games, world, osaka, olympics
Topic 15: olympics, athletes, tokyo, olympic, games
Topic 16: sports, world, one, hockey, city
Topic 17: chand, dhyan, award, ratna, khel
Topic 18: medal, dahiya, wrestling, wrestler, world
Topic 19: india, team, penalty, goal, first
Topic 20: singh, hockey, team, medal, olympics
Topic 21: chanu, mirabai, medal, minister, sports
Topic 22: sports, games, athletes, olympics, training
Topic 23: team, hockey, indian, india, women's
Topic 24: aditi, gold, medal, olympics, ashok
Topic 25: rs, crore, cash, announced, lakh
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Completed E-Step (0 seconds).
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Completing Iteration 16 (approx. per word bound = -7.125, relative change = 1.348e-04)
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Completing Iteration 17 (approx. per word bound = -7.125, relative change = 1.112e-04)
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Completing Iteration 18 (approx. per word bound = -7.124, relative change = 9.660e-05)
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Completed E-Step (0 seconds).
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Completing Iteration 19 (approx. per word bound = -7.123, relative change = 9.258e-05)
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Completed E-Step (0 seconds).
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Completing Iteration 20 (approx. per word bound = -7.123, relative change = 8.048e-05)
Topic 1: manika, indian, singles, tennis, round
Topic 2: throw, neeraj, gold, chopra, medal
Topic 3: chanu, mirabai, medal, silver, tokyo
Topic 4: medal, gold, neeraj, chopra, win
Topic 5: hockey, india, team, medal, olympics
Topic 6: women, olympics, hockey, men, first
Topic 7: men's, team, round, women's, world
Topic 8: hockey, team, indian, players, women's
Topic 9: sindhu, pv, medal, olympics, badminton
Topic 10: olympics, video, shared, tokyo, post
Topic 11: lovlina, medal, borgohain, boxing, boxer
Topic 12: biles, olympics, one, like, world
Topic 13: olympic, hai, sharma, back, years
Topic 14: tokyo, games, world, osaka, olympics
Topic 15: olympics, athletes, tokyo, olympic, games
Topic 16: sports, world, one, city, hockey
Topic 17: chand, dhyan, award, ratna, khel
Topic 18: dahiya, medal, wrestling, wrestler, world
Topic 19: india, penalty, team, goal, first
Topic 20: singh, hockey, team, medal, olympics
Topic 21: chanu, mirabai, medal, minister, sports
Topic 22: sports, games, athletes, olympics, training
Topic 23: team, hockey, indian, india, women's
Topic 24: aditi, gold, medal, olympics, ashok
Topic 25: rs, crore, cash, announced, lakh
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Completed E-Step (0 seconds).
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Completing Iteration 21 (approx. per word bound = -7.122, relative change = 8.070e-05)
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Completing Iteration 22 (approx. per word bound = -7.122, relative change = 7.150e-05)
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Completing Iteration 23 (approx. per word bound = -7.121, relative change = 5.665e-05)
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Completing Iteration 24 (approx. per word bound = -7.121, relative change = 5.717e-05)
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Completed E-Step (0 seconds).
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Completing Iteration 25 (approx. per word bound = -7.120, relative change = 6.579e-05)
Topic 1: manika, indian, tennis, singles, round
Topic 2: neeraj, throw, gold, chopra, medal
Topic 3: chanu, mirabai, medal, silver, tokyo
Topic 4: medal, gold, neeraj, win, chopra
Topic 5: hockey, india, team, medal, olympics
Topic 6: women, olympics, men, hockey, first
Topic 7: men's, team, round, women's, world
Topic 8: hockey, team, indian, players, women's
Topic 9: sindhu, pv, medal, olympics, badminton
Topic 10: olympics, video, shared, tokyo, post
Topic 11: lovlina, medal, borgohain, boxing, boxer
Topic 12: biles, olympics, one, like, world
Topic 13: olympic, hai, sharma, back, years
Topic 14: tokyo, games, world, osaka, olympics
Topic 15: olympics, athletes, tokyo, olympic, games
Topic 16: sports, world, one, city, hockey
Topic 17: chand, dhyan, award, ratna, khel
Topic 18: dahiya, medal, wrestling, wrestler, world
Topic 19: india, penalty, team, goal, first
Topic 20: singh, hockey, team, medal, olympics
Topic 21: chanu, mirabai, minister, medal, sports
Topic 22: sports, games, athletes, olympics, training
Topic 23: team, hockey, indian, india, women's
Topic 24: aditi, gold, medal, olympics, ashok
Topic 25: rs, crore, cash, announced, lakh
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Completed E-Step (0 seconds).
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Completing Iteration 26 (approx. per word bound = -7.120, relative change = 4.868e-05)
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Completing Iteration 27 (approx. per word bound = -7.120, relative change = 3.636e-05)
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Completing Iteration 28 (approx. per word bound = -7.120, relative change = 3.478e-05)
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Completing Iteration 29 (approx. per word bound = -7.119, relative change = 3.884e-05)
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Completing Iteration 30 (approx. per word bound = -7.119, relative change = 5.479e-05)
Topic 1: manika, indian, tennis, singles, round
Topic 2: neeraj, gold, throw, chopra, medal
Topic 3: chanu, mirabai, medal, silver, tokyo
Topic 4: medal, gold, neeraj, win, proud
Topic 5: hockey, india, team, medal, olympics
Topic 6: women, olympics, men, first, hockey
Topic 7: men's, team, round, women's, world
Topic 8: hockey, team, indian, players, women's
Topic 9: sindhu, pv, medal, olympics, badminton
Topic 10: olympics, video, shared, tokyo, post
Topic 11: lovlina, medal, borgohain, boxing, boxer
Topic 12: biles, olympics, like, one, world
Topic 13: olympic, hai, sharma, back, years
Topic 14: tokyo, games, world, osaka, olympics
Topic 15: olympics, athletes, tokyo, olympic, games
Topic 16: sports, world, one, city, hockey
Topic 17: chand, dhyan, award, ratna, khel
Topic 18: dahiya, medal, wrestling, wrestler, world
Topic 19: india, penalty, team, goal, first
Topic 20: singh, hockey, team, medal, olympics
Topic 21: chanu, mirabai, minister, medal, sports
Topic 22: sports, games, athletes, olympics, training
Topic 23: team, hockey, indian, india, women's
Topic 24: aditi, gold, medal, olympics, ashok
Topic 25: rs, crore, cash, announced, lakh
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Completing Iteration 31 (approx. per word bound = -7.119, relative change = 5.450e-05)
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Completing Iteration 32 (approx. per word bound = -7.118, relative change = 5.022e-05)
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Completing Iteration 33 (approx. per word bound = -7.118, relative change = 7.161e-05)
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Completing Iteration 34 (approx. per word bound = -7.117, relative change = 8.876e-05)
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Completing Iteration 35 (approx. per word bound = -7.117, relative change = 6.408e-05)
Topic 1: tennis, indian, manika, singles, round
Topic 2: gold, neeraj, chopra, throw, medal
Topic 3: chanu, mirabai, medal, silver, tokyo
Topic 4: medal, gold, win, neeraj, proud
Topic 5: hockey, india, team, medal, olympics
Topic 6: women, olympics, men, first, india
Topic 7: men's, team, round, women's, world
Topic 8: hockey, team, indian, players, women's
Topic 9: sindhu, pv, medal, olympics, badminton
Topic 10: olympics, video, shared, tokyo, post
Topic 11: lovlina, medal, borgohain, boxing, boxer
Topic 12: biles, olympics, like, one, world
Topic 13: olympic, hai, sharma, back, years
Topic 14: tokyo, games, world, osaka, olympics
Topic 15: olympics, athletes, tokyo, olympic, games
Topic 16: sports, world, one, city, hockey
Topic 17: chand, dhyan, award, ratna, khel
Topic 18: dahiya, medal, wrestling, wrestler, world
Topic 19: india, penalty, team, goal, first
Topic 20: singh, hockey, team, medal, olympics
Topic 21: chanu, mirabai, minister, medal, sports
Topic 22: sports, games, athletes, olympics, training
Topic 23: team, hockey, indian, india, women's
Topic 24: aditi, gold, medal, olympics, ashok
Topic 25: rs, crore, cash, announced, lakh
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Completed E-Step (0 seconds).
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Completing Iteration 36 (approx. per word bound = -7.116, relative change = 4.736e-05)
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Completing Iteration 37 (approx. per word bound = -7.116, relative change = 4.101e-05)
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Completing Iteration 38 (approx. per word bound = -7.116, relative change = 5.744e-05)
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Completing Iteration 39 (approx. per word bound = -7.115, relative change = 4.713e-05)
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Completing Iteration 40 (approx. per word bound = -7.115, relative change = 3.472e-05)
Topic 1: tennis, indian, manika, singles, round
Topic 2: gold, neeraj, chopra, throw, medal
Topic 3: chanu, mirabai, medal, silver, tokyo
Topic 4: medal, gold, win, neeraj, proud
Topic 5: hockey, india, team, medal, olympics
Topic 6: women, olympics, men, first, india
Topic 7: men's, team, round, women's, world
Topic 8: hockey, team, indian, players, women's
Topic 9: sindhu, pv, medal, olympics, badminton
Topic 10: olympics, video, shared, tokyo, post
Topic 11: lovlina, medal, borgohain, boxing, boxer
Topic 12: biles, olympics, like, one, world
Topic 13: olympic, hai, sharma, back, years
Topic 14: tokyo, games, world, osaka, olympics
Topic 15: olympics, athletes, tokyo, olympic, games
Topic 16: sports, world, one, city, hockey
Topic 17: chand, dhyan, award, ratna, khel
Topic 18: dahiya, medal, wrestling, wrestler, bajrang
Topic 19: india, penalty, team, goal, first
Topic 20: singh, hockey, team, medal, olympics
Topic 21: chanu, mirabai, minister, medal, sports
Topic 22: sports, games, olympics, athletes, world
Topic 23: team, hockey, indian, india, women's
Topic 24: aditi, gold, medal, olympics, ashok
Topic 25: rs, crore, cash, announced, lakh
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Completing Iteration 41 (approx. per word bound = -7.115, relative change = 4.368e-05)
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Completing Iteration 42 (approx. per word bound = -7.114, relative change = 6.191e-05)
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Completing Iteration 43 (approx. per word bound = -7.114, relative change = 5.828e-05)
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Completing Iteration 44 (approx. per word bound = -7.113, relative change = 3.983e-05)
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Completing Iteration 45 (approx. per word bound = -7.113, relative change = 2.958e-05)
Topic 1: tennis, indian, manika, singles, round
Topic 2: gold, neeraj, chopra, throw, medal
Topic 3: chanu, mirabai, medal, silver, tokyo
Topic 4: medal, gold, win, proud, neeraj
Topic 5: hockey, india, team, medal, olympics
Topic 6: women, olympics, men, first, india
Topic 7: men's, team, round, women's, world
Topic 8: hockey, team, indian, players, women's
Topic 9: sindhu, pv, medal, olympics, badminton
Topic 10: olympics, video, shared, tokyo, post
Topic 11: lovlina, medal, borgohain, boxing, boxer
Topic 12: biles, olympics, like, one, world
Topic 13: olympic, hai, sharma, back, years
Topic 14: tokyo, games, world, osaka, olympics
Topic 15: olympics, athletes, tokyo, olympic, games
Topic 16: sports, world, one, city, hockey
Topic 17: chand, dhyan, award, ratna, khel
Topic 18: dahiya, medal, wrestling, wrestler, bajrang
Topic 19: india, penalty, team, goal, first
Topic 20: singh, hockey, team, medal, olympics
Topic 21: chanu, mirabai, minister, medal, sports
Topic 22: sports, games, olympics, athletes, world
Topic 23: team, hockey, indian, india, women's
Topic 24: aditi, gold, medal, olympics, ashok
Topic 25: rs, crore, cash, announced, lakh
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Completed E-Step (0 seconds).
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Completing Iteration 46 (approx. per word bound = -7.113, relative change = 2.555e-05)
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Completing Iteration 47 (approx. per word bound = -7.113, relative change = 2.385e-05)
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Completing Iteration 48 (approx. per word bound = -7.113, relative change = 1.709e-05)
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Completing Iteration 49 (approx. per word bound = -7.113, relative change = 1.822e-05)
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Completing Iteration 50 (approx. per word bound = -7.113, relative change = 1.382e-05)
Topic 1: tennis, indian, manika, singles, round
Topic 2: gold, chopra, neeraj, throw, medal
Topic 3: chanu, mirabai, medal, silver, tokyo
Topic 4: medal, gold, win, proud, olympics
Topic 5: hockey, india, team, medal, olympics
Topic 6: women, olympics, men, first, india
Topic 7: men's, team, round, women's, world
Topic 8: hockey, team, indian, players, women's
Topic 9: sindhu, pv, medal, olympics, badminton
Topic 10: olympics, video, shared, tokyo, post
Topic 11: lovlina, medal, borgohain, boxing, boxer
Topic 12: biles, olympics, like, one, people
Topic 13: olympic, hai, sharma, back, years
Topic 14: tokyo, games, world, osaka, olympics
Topic 15: olympics, athletes, tokyo, olympic, games
Topic 16: sports, world, one, city, hockey
Topic 17: chand, dhyan, award, ratna, khel
Topic 18: dahiya, medal, wrestling, wrestler, bajrang
Topic 19: india, penalty, team, goal, first
Topic 20: singh, hockey, team, medal, olympics
Topic 21: chanu, mirabai, minister, medal, sports
Topic 22: sports, games, olympics, athletes, world
Topic 23: team, hockey, indian, india, women's
Topic 24: aditi, gold, medal, olympics, ashok
Topic 25: rs, crore, cash, announced, lakh
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Completed E-Step (0 seconds).
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Completing Iteration 51 (approx. per word bound = -7.112, relative change = 1.283e-05)
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Completing Iteration 52 (approx. per word bound = -7.112, relative change = 1.342e-05)
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Completing Iteration 53 (approx. per word bound = -7.112, relative change = 1.461e-05)
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Completing Iteration 54 (approx. per word bound = -7.112, relative change = 1.436e-05)
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Completing Iteration 55 (approx. per word bound = -7.112, relative change = 1.572e-05)
Topic 1: tennis, indian, manika, singles, round
Topic 2: gold, chopra, neeraj, throw, medal
Topic 3: chanu, mirabai, medal, silver, tokyo
Topic 4: medal, win, gold, proud, olympics
Topic 5: hockey, team, india, medal, olympics
Topic 6: women, olympics, men, first, india
Topic 7: men's, team, round, women's, world
Topic 8: hockey, team, indian, players, women's
Topic 9: sindhu, pv, medal, olympics, badminton
Topic 10: olympics, video, shared, tokyo, post
Topic 11: lovlina, medal, borgohain, boxing, boxer
Topic 12: biles, olympics, like, one, people
Topic 13: olympic, hai, sharma, back, years
Topic 14: tokyo, games, world, osaka, olympic
Topic 15: olympics, athletes, tokyo, olympic, games
Topic 16: sports, world, one, city, hockey
Topic 17: chand, dhyan, award, ratna, khel
Topic 18: dahiya, medal, wrestling, wrestler, bajrang
Topic 19: india, penalty, team, goal, first
Topic 20: singh, hockey, team, medal, olympics
Topic 21: chanu, mirabai, minister, medal, sports
Topic 22: sports, games, olympics, athletes, world
Topic 23: team, hockey, indian, india, women's
Topic 24: aditi, gold, medal, olympics, ashok
Topic 25: rs, crore, announced, cash, olympics
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Completed E-Step (0 seconds).
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Completing Iteration 56 (approx. per word bound = -7.112, relative change = 1.645e-05)
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Completing Iteration 57 (approx. per word bound = -7.112, relative change = 1.607e-05)
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Completing Iteration 58 (approx. per word bound = -7.112, relative change = 1.343e-05)
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Completing Iteration 59 (approx. per word bound = -7.112, relative change = 1.392e-05)
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Completing Iteration 60 (approx. per word bound = -7.112, relative change = 1.435e-05)
Topic 1: tennis, indian, manika, singles, round
Topic 2: gold, chopra, neeraj, throw, medal
Topic 3: chanu, mirabai, medal, silver, tokyo
Topic 4: medal, win, proud, gold, olympics
Topic 5: hockey, team, india, medal, olympics
Topic 6: women, olympics, men, first, india
Topic 7: men's, team, round, women's, world
Topic 8: hockey, team, indian, players, women's
Topic 9: sindhu, pv, medal, olympics, badminton
Topic 10: olympics, video, shared, tokyo, post
Topic 11: lovlina, medal, borgohain, boxing, boxer
Topic 12: biles, olympics, like, one, people
Topic 13: olympic, hai, sharma, back, years
Topic 14: tokyo, games, world, osaka, olympic
Topic 15: olympics, athletes, tokyo, olympic, games
Topic 16: sports, world, one, city, hockey
Topic 17: chand, dhyan, award, ratna, khel
Topic 18: dahiya, medal, wrestling, wrestler, bajrang
Topic 19: india, penalty, team, goal, first
Topic 20: singh, hockey, team, medal, olympics
Topic 21: chanu, mirabai, minister, medal, sports
Topic 22: sports, games, olympics, athletes, world
Topic 23: team, hockey, indian, india, women's
Topic 24: aditi, gold, medal, olympics, ashok
Topic 25: rs, crore, announced, cash, olympics
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Completed E-Step (0 seconds).
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Completing Iteration 61 (approx. per word bound = -7.111, relative change = 1.311e-05)
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Completing Iteration 62 (approx. per word bound = -7.111, relative change = 1.074e-05)
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Completing Iteration 63 (approx. per word bound = -7.111, relative change = 1.020e-05)
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Completing Iteration 64 (approx. per word bound = -7.111, relative change = 1.062e-05)
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Completing Iteration 65 (approx. per word bound = -7.111, relative change = 1.041e-05)
Topic 1: tennis, indian, manika, singles, round
Topic 2: gold, chopra, neeraj, throw, medal
Topic 3: chanu, mirabai, medal, silver, tokyo
Topic 4: medal, win, proud, gold, olympics
Topic 5: hockey, team, india, medal, olympics
Topic 6: women, olympics, men, first, india
Topic 7: men's, team, round, women's, world
Topic 8: hockey, team, indian, players, women's
Topic 9: sindhu, pv, medal, olympics, badminton
Topic 10: olympics, video, shared, tokyo, post
Topic 11: lovlina, medal, borgohain, boxing, boxer
Topic 12: biles, olympics, like, one, people
Topic 13: olympic, hai, sharma, back, years
Topic 14: tokyo, games, world, osaka, olympic
Topic 15: olympics, athletes, tokyo, olympic, games
Topic 16: sports, world, one, city, hockey
Topic 17: chand, dhyan, award, ratna, khel
Topic 18: dahiya, medal, wrestler, wrestling, bajrang
Topic 19: india, penalty, team, goal, first
Topic 20: singh, hockey, team, medal, olympics
Topic 21: chanu, mirabai, minister, medal, sports
Topic 22: sports, games, olympics, athletes, world
Topic 23: team, hockey, indian, india, women's
Topic 24: aditi, gold, medal, olympics, ashok
Topic 25: rs, crore, cash, announced, olympics
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Completed E-Step (0 seconds).
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Completing Iteration 66 (approx. per word bound = -7.111, relative change = 1.040e-05)
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Completing Iteration 67 (approx. per word bound = -7.111, relative change = 1.280e-05)
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Completing Iteration 68 (approx. per word bound = -7.111, relative change = 1.405e-05)
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Completing Iteration 69 (approx. per word bound = -7.111, relative change = 1.625e-05)
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Completing Iteration 70 (approx. per word bound = -7.111, relative change = 1.805e-05)
Topic 1: tennis, indian, manika, singles, round
Topic 2: gold, chopra, neeraj, throw, medal
Topic 3: chanu, mirabai, medal, silver, tokyo
Topic 4: medal, win, proud, gold, olympics
Topic 5: hockey, team, india, medal, olympics
Topic 6: women, olympics, men, first, india
Topic 7: men's, team, round, women's, world
Topic 8: hockey, team, indian, players, women's
Topic 9: sindhu, pv, medal, olympics, badminton
Topic 10: olympics, video, shared, tokyo, post
Topic 11: lovlina, medal, borgohain, boxing, boxer
Topic 12: biles, olympics, like, one, people
Topic 13: olympic, hai, sharma, back, years
Topic 14: tokyo, games, world, osaka, olympic
Topic 15: olympics, athletes, tokyo, olympic, games
Topic 16: sports, world, one, city, hockey
Topic 17: chand, dhyan, award, ratna, khel
Topic 18: dahiya, medal, wrestler, wrestling, bajrang
Topic 19: india, penalty, team, goal, first
Topic 20: singh, hockey, team, medal, olympics
Topic 21: chanu, mirabai, minister, medal, sports
Topic 22: sports, games, olympics, athletes, world
Topic 23: team, hockey, indian, india, women's
Topic 24: aditi, gold, medal, olympics, ashok
Topic 25: rs, crore, cash, announced, olympics
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Completed E-Step (0 seconds).
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Completing Iteration 71 (approx. per word bound = -7.111, relative change = 1.685e-05)
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Completing Iteration 72 (approx. per word bound = -7.110, relative change = 1.233e-05)
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Completing Iteration 73 (approx. per word bound = -7.110, relative change = 1.196e-05)
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Completing Iteration 74 (approx. per word bound = -7.110, relative change = 1.500e-05)
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Completing Iteration 75 (approx. per word bound = -7.110, relative change = 1.853e-05)
Topic 1: tennis, indian, manika, singles, round
Topic 2: gold, chopra, neeraj, throw, medal
Topic 3: chanu, mirabai, medal, silver, tokyo
Topic 4: medal, win, proud, gold, congratulations
Topic 5: hockey, team, india, medal, olympics
Topic 6: women, olympics, men, first, india
Topic 7: men's, team, round, women's, world
Topic 8: hockey, team, indian, players, women's
Topic 9: sindhu, pv, medal, olympics, badminton
Topic 10: olympics, video, shared, tokyo, post
Topic 11: lovlina, medal, borgohain, boxing, boxer
Topic 12: biles, olympics, like, one, people
Topic 13: olympic, hai, sharma, back, years
Topic 14: tokyo, games, world, osaka, olympic
Topic 15: olympics, athletes, tokyo, olympic, games
Topic 16: sports, world, one, city, hockey
Topic 17: chand, dhyan, award, ratna, khel
Topic 18: dahiya, medal, wrestler, wrestling, bajrang
Topic 19: india, penalty, team, goal, first
Topic 20: singh, hockey, team, medal, olympics
Topic 21: chanu, mirabai, minister, medal, sports
Topic 22: sports, games, olympics, athletes, world
Topic 23: team, hockey, indian, india, women's
Topic 24: aditi, gold, medal, olympics, ashok
Topic 25: rs, crore, cash, announced, olympics
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Completed E-Step (0 seconds).
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Completing Iteration 76 (approx. per word bound = -7.110, relative change = 1.743e-05)
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Completed E-Step (0 seconds).
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Completing Iteration 77 (approx. per word bound = -7.110, relative change = 1.064e-05)
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Completed E-Step (0 seconds).
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Model Converged
Beginning Spectral Initialization
Calculating the gram matrix...
Using only 10000 most frequent terms during initialization...
Finding anchor words...
..................................................
Recovering initialization...
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Initialization complete.
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Completed E-Step (1 seconds).
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Completing Iteration 1 (approx. per word bound = -7.942)
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Completed E-Step (1 seconds).
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Completing Iteration 2 (approx. per word bound = -6.981, relative change = 1.210e-01)
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Completed E-Step (1 seconds).
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Completing Iteration 3 (approx. per word bound = -6.864, relative change = 1.684e-02)
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Completed E-Step (1 seconds).
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Completing Iteration 4 (approx. per word bound = -6.835, relative change = 4.225e-03)
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Completed E-Step (1 seconds).
Completed M-Step.
Completing Iteration 5 (approx. per word bound = -6.822, relative change = 1.812e-03)
Topic 1: manika, round, women's, singles, batra
Topic 2: throw, neeraj, javelin, gold, chopra
Topic 3: medal, tokyo, one, chanu, india's
Topic 4: neeraj, chopra, gold, medal, win
Topic 5: hockey, india, team, match, medal
Topic 6: women, olympics, india, men, first
Topic 7: men's, team, round, indian, india
Topic 8: hockey, team, women's, indian, players
Topic 9: sindhu, medal, bronze, olympics, indian
Topic 10: hockey, match, team, india, women's
Topic 11: lovlina, borgohain, medal, boxing, boxer
Topic 12: olympics, stories, samantha, like, watch
Topic 13: sharma, olympic, back, made, kim
Topic 14: osaka, world, first, gold, tokyo
Topic 15: olympics, medal, tokyo, olympic, medals
Topic 16: sports, world, hockey, city, indian
Topic 17: dhyan, chand, award, ratna, khel
Topic 18: medal, bajrang, bronze, wrestler, malik
Topic 19: india, team, penalty, first, indian
Topic 20: singh, hockey, india, medal, team
Topic 21: minister, government, chief, chanu, house
Topic 22: chopra, army, olympics, neeraj, sports
Topic 23: team, hockey, indian, india, women's
Topic 24: gold, medal, olympics, tokyo, final
Topic 25: rs, cash, crore, chopra, announced
Topic 26: sindhu, pv, medal, olympics, win
Topic 27: mental, bjp, body, takes, physical
Topic 28: world, match, like, time, first
Topic 29: women's, men's, team, ist, air
Topic 30: medal, proud, india, congratulations, olympics
Topic 31: team, hockey, family, vandana, caste
Topic 32: sports, games, olympic, athletes, world
Topic 33: team, india, sreejesh, hockey, medal
Topic 34: biles, film, proud, abhimanyu, right
Topic 35: medal, first, games, olympics, chanu
Topic 36: brands, brand, pizza, chanu, domino's
Topic 37: athletes, olympics, games, sports, tokyo
Topic 38: coach, tokyo, world, olympics, team
Topic 39: dahiya, village, world, medal, gold
Topic 40: dahiya, kumar, medal, wrestler, olympic
Topic 41: olympics, tokyo, ceremony, games, athletes
Topic 42: hockey, team, medal, singh, players
Topic 43: sindhu, world, indian, game, yamaguchi
Topic 44: hockey, team, coach, family, game
Topic 45: world, athletes, like, olympics, olympic
Topic 46: india, indian, chanu, medal, olympics
Topic 47: gold, medal, olympics, olympic, media
Topic 48: mirabai, medal, chanu, silver, olympics
Topic 49: aditi, ashok, round, golf, two
Topic 50: indian, students, tokyo, olympics, singh
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Completed E-Step (1 seconds).
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Completing Iteration 6 (approx. per word bound = -6.816, relative change = 8.724e-04)
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Completed E-Step (1 seconds).
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Completing Iteration 7 (approx. per word bound = -6.813, relative change = 5.062e-04)
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Completed E-Step (1 seconds).
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Completing Iteration 8 (approx. per word bound = -6.811, relative change = 3.038e-04)
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Completed E-Step (1 seconds).
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Completing Iteration 9 (approx. per word bound = -6.809, relative change = 2.077e-04)
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Completed E-Step (1 seconds).
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Completing Iteration 10 (approx. per word bound = -6.808, relative change = 1.572e-04)
Topic 1: manika, round, singles, women's, game
Topic 2: throw, neeraj, gold, javelin, chopra
Topic 3: medal, tokyo, chanu, one, india's
Topic 4: neeraj, gold, chopra, medal, olympics
Topic 5: hockey, india, team, match, men's
Topic 6: women, olympics, india, men, first
Topic 7: men's, team, round, indian, das
Topic 8: hockey, team, women's, indian, players
Topic 9: sindhu, medal, bronze, olympics, indian
Topic 10: hockey, match, team, india, women's
Topic 11: lovlina, borgohain, medal, boxing, boxer
Topic 12: olympics, stories, like, watch, book
Topic 13: sharma, olympic, back, made, medal
Topic 14: osaka, world, first, gold, tokyo
Topic 15: olympics, medal, tokyo, olympic, medals
Topic 16: sports, hockey, world, city, indian
Topic 17: dhyan, chand, award, ratna, khel
Topic 18: medal, bajrang, bronze, wrestler, punia
Topic 19: india, penalty, team, first, indian
Topic 20: singh, hockey, india, medal, team
Topic 21: minister, government, chanu, chief, house
Topic 22: chopra, army, neeraj, olympics, sports
Topic 23: team, hockey, indian, india, women's
Topic 24: gold, medal, olympics, tokyo, won
Topic 25: rs, cash, crore, announced, chopra
Topic 26: sindhu, pv, medal, olympics, bronze
Topic 27: mental, bjp, takes, body, opposition
Topic 28: world, like, match, sindhu, olympics
Topic 29: men's, women's, team, ist, air
Topic 30: medal, congratulations, india, proud, olympics
Topic 31: team, hockey, family, vandana, caste
Topic 32: sports, games, athletes, olympic, world
Topic 33: team, india, sreejesh, medal, hockey
Topic 34: proud, biles, film, abhimanyu, right
Topic 35: medal, first, games, olympics, chanu
Topic 36: brands, brand, pizza, chanu, domino's
Topic 37: athletes, olympics, games, sports, tokyo
Topic 38: coach, world, tokyo, olympics, pistol
Topic 39: dahiya, village, medal, gold, world
Topic 40: dahiya, kumar, medal, olympic, wrestler
Topic 41: olympics, tokyo, ceremony, games, athletes
Topic 42: hockey, team, medal, singh, olympics
Topic 43: sindhu, world, indian, game, tai
Topic 44: hockey, team, family, coach, game
Topic 45: world, athletes, like, olympic, olympics
Topic 46: india, indian, chanu, medal, olympics
Topic 47: gold, medal, olympics, olympic, media
Topic 48: mirabai, medal, chanu, silver, olympics
Topic 49: aditi, ashok, round, golf, medal
Topic 50: indian, students, tokyo, olympics, singh
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Completing Iteration 11 (approx. per word bound = -6.807, relative change = 1.167e-04)
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Completing Iteration 13 (approx. per word bound = -6.806, relative change = 7.697e-05)
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Completing Iteration 15 (approx. per word bound = -6.805, relative change = 9.755e-05)
Topic 1: manika, round, singles, women's, game
Topic 2: throw, neeraj, gold, javelin, chopra
Topic 3: medal, tokyo, chanu, one, india's
Topic 4: neeraj, gold, chopra, medal, olympics
Topic 5: hockey, india, team, match, medal
Topic 6: women, olympics, india, men, first
Topic 7: men's, team, round, indian, das
Topic 8: hockey, team, women's, odisha, players
Topic 9: sindhu, medal, bronze, olympics, indian
Topic 10: hockey, match, team, india, women's
Topic 11: lovlina, borgohain, medal, boxing, boxer
Topic 12: olympics, like, stories, watch, book
Topic 13: sharma, olympic, back, made, medal
Topic 14: osaka, world, first, gold, tokyo
Topic 15: olympics, medal, tokyo, olympic, medals
Topic 16: sports, hockey, world, city, indian
Topic 17: dhyan, chand, award, ratna, khel
Topic 18: bajrang, medal, wrestler, punia, bronze
Topic 19: india, penalty, team, first, goal
Topic 20: singh, hockey, medal, india, team
Topic 21: minister, government, house, chief, chanu
Topic 22: chopra, army, neeraj, olympics, gold
Topic 23: team, hockey, indian, india, women's
Topic 24: gold, medal, olympics, tokyo, won
Topic 25: rs, cash, crore, announced, chopra
Topic 26: sindhu, pv, medal, olympics, bronze
Topic 27: mental, bjp, takes, body, congress
Topic 28: world, sindhu, like, match, olympics
Topic 29: men's, women's, team, ist, air
Topic 30: medal, congratulations, india, proud, olympics
Topic 31: team, hockey, family, vandana, caste
Topic 32: sports, games, athletes, olympic, world
Topic 33: team, india, sreejesh, medal, hockey
Topic 34: proud, biles, film, abhimanyu, right
Topic 35: medal, first, games, olympics, chanu
Topic 36: brands, brand, pizza, chanu, domino's
Topic 37: athletes, olympics, games, sports, tokyo
Topic 38: coach, world, olympics, tokyo, pistol
Topic 39: dahiya, village, medal, gold, world
Topic 40: dahiya, kumar, medal, olympic, wrestler
Topic 41: olympics, tokyo, ceremony, games, athletes
Topic 42: hockey, team, medal, singh, players
Topic 43: sindhu, world, indian, game, tai
Topic 44: hockey, team, family, coach, game
Topic 45: world, athletes, like, olympic, olympics
Topic 46: india, chanu, indian, medal, olympics
Topic 47: gold, medal, olympics, olympic, people
Topic 48: mirabai, medal, chanu, silver, olympics
Topic 49: aditi, ashok, round, golf, medal
Topic 50: indian, students, tokyo, olympics, singh
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Completing Iteration 16 (approx. per word bound = -6.805, relative change = 6.267e-05)
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Completing Iteration 17 (approx. per word bound = -6.804, relative change = 4.915e-05)
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Completing Iteration 18 (approx. per word bound = -6.804, relative change = 4.120e-05)
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Completing Iteration 19 (approx. per word bound = -6.804, relative change = 4.297e-05)
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Completing Iteration 20 (approx. per word bound = -6.803, relative change = 2.711e-05)
Topic 1: manika, round, singles, women's, game
Topic 2: throw, neeraj, gold, javelin, chopra
Topic 3: medal, tokyo, chanu, one, india's
Topic 4: neeraj, gold, chopra, medal, olympics
Topic 5: hockey, india, team, match, medal
Topic 6: women, olympics, india, men, first
Topic 7: team, men's, indian, round, das
Topic 8: hockey, team, women's, odisha, players
Topic 9: sindhu, medal, bronze, olympics, indian
Topic 10: hockey, match, team, india, women's
Topic 11: lovlina, borgohain, medal, boxing, boxer
Topic 12: olympics, like, watch, stories, book
Topic 13: sharma, olympic, back, made, medal
Topic 14: osaka, world, first, gold, tokyo
Topic 15: olympics, medal, tokyo, olympic, medals
Topic 16: sports, hockey, world, city, indian
Topic 17: dhyan, chand, award, ratna, khel
Topic 18: bajrang, medal, punia, wrestler, bronze
Topic 19: india, penalty, team, first, goal
Topic 20: singh, hockey, medal, india, team
Topic 21: minister, government, house, chief, chanu
Topic 22: chopra, army, neeraj, olympics, gold
Topic 23: team, hockey, indian, india, women's
Topic 24: gold, medal, olympics, tokyo, won
Topic 25: rs, cash, crore, announced, medal
Topic 26: sindhu, pv, medal, olympics, bronze
Topic 27: mental, bjp, takes, body, congress
Topic 28: world, sindhu, like, match, olympics
Topic 29: men's, women's, team, ist, air
Topic 30: medal, congratulations, proud, india, olympics
Topic 31: team, hockey, family, vandana, caste
Topic 32: sports, games, athletes, olympic, world
Topic 33: team, india, sreejesh, medal, indian
Topic 34: proud, biles, film, abhimanyu, right
Topic 35: medal, first, games, olympics, chanu
Topic 36: brands, brand, pizza, chanu, domino's
Topic 37: athletes, olympics, games, sports, tokyo
Topic 38: coach, world, olympics, pistol, tokyo
Topic 39: dahiya, village, medal, gold, world
Topic 40: dahiya, kumar, medal, olympic, wrestler
Topic 41: olympics, tokyo, ceremony, games, athletes
Topic 42: hockey, team, singh, medal, players
Topic 43: sindhu, world, indian, game, tai
Topic 44: hockey, team, family, coach, game
Topic 45: world, athletes, like, olympic, chand
Topic 46: india, chanu, indian, medal, olympics
Topic 47: gold, medal, olympics, olympic, people
Topic 48: mirabai, medal, chanu, silver, olympics
Topic 49: aditi, ashok, round, golf, medal
Topic 50: indian, students, tokyo, olympics, singh
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Completing Iteration 21 (approx. per word bound = -6.803, relative change = 4.112e-05)
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Completing Iteration 25 (approx. per word bound = -6.802, relative change = 2.659e-05)
Topic 1: manika, round, singles, women's, game
Topic 2: throw, neeraj, gold, javelin, chopra
Topic 3: medal, tokyo, chanu, one, india's
Topic 4: neeraj, gold, chopra, medal, olympics
Topic 5: hockey, india, team, match, medal
Topic 6: women, olympics, india, men, first
Topic 7: team, men's, indian, round, das
Topic 8: hockey, team, women's, odisha, players
Topic 9: sindhu, medal, bronze, olympics, indian
Topic 10: hockey, match, team, india, women's
Topic 11: lovlina, borgohain, medal, boxing, boxer
Topic 12: olympics, like, stories, watch, book
Topic 13: sharma, olympic, back, made, medal
Topic 14: osaka, world, first, gold, tokyo
Topic 15: olympics, medal, tokyo, olympic, medals
Topic 16: sports, hockey, world, city, indian
Topic 17: dhyan, chand, award, ratna, khel
Topic 18: bajrang, medal, punia, wrestler, bronze
Topic 19: india, penalty, team, goal, first
Topic 20: singh, hockey, medal, india, team
Topic 21: minister, government, house, chanu, chief
Topic 22: chopra, army, neeraj, olympics, gold
Topic 23: team, hockey, indian, india, women's
Topic 24: gold, medal, olympics, tokyo, won
Topic 25: rs, cash, crore, announced, medal
Topic 26: sindhu, pv, medal, olympics, win
Topic 27: mental, bjp, takes, body, congress
Topic 28: world, sindhu, like, match, olympics
Topic 29: men's, women's, ist, team, air
Topic 30: medal, congratulations, proud, india, olympics
Topic 31: team, hockey, family, vandana, caste
Topic 32: sports, games, athletes, olympic, world
Topic 33: team, india, sreejesh, medal, indian
Topic 34: proud, biles, film, abhimanyu, right
Topic 35: medal, first, games, olympics, chanu
Topic 36: brands, brand, pizza, chanu, domino's
Topic 37: athletes, olympics, games, sports, tokyo
Topic 38: coach, world, pistol, olympics, shooters
Topic 39: dahiya, village, medal, gold, world
Topic 40: dahiya, kumar, medal, olympic, wrestler
Topic 41: olympics, tokyo, ceremony, games, athletes
Topic 42: hockey, team, singh, medal, players
Topic 43: sindhu, world, indian, game, tai
Topic 44: hockey, team, family, coach, game
Topic 45: world, athletes, like, olympic, chand
Topic 46: india, chanu, indian, medal, olympics
Topic 47: gold, medal, olympics, olympic, people
Topic 48: mirabai, medal, chanu, silver, olympics
Topic 49: aditi, ashok, round, golf, medal
Topic 50: indian, students, tokyo, olympics, singh
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Completing Iteration 26 (approx. per word bound = -6.802, relative change = 2.003e-05)
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Completing Iteration 27 (approx. per word bound = -6.802, relative change = 2.024e-05)
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Completing Iteration 30 (approx. per word bound = -6.802, relative change = 1.487e-05)
Topic 1: manika, round, singles, game, women's
Topic 2: throw, neeraj, gold, javelin, chopra
Topic 3: medal, tokyo, chanu, one, india's
Topic 4: neeraj, gold, chopra, medal, olympics
Topic 5: hockey, india, team, match, medal
Topic 6: women, olympics, india, men, first
Topic 7: team, indian, men's, round, das
Topic 8: hockey, team, women's, odisha, players
Topic 9: sindhu, medal, bronze, olympics, indian
Topic 10: hockey, match, team, women's, india
Topic 11: lovlina, borgohain, medal, boxing, boxer
Topic 12: olympics, like, stories, watch, book
Topic 13: sharma, olympic, back, made, medal
Topic 14: osaka, world, first, gold, tokyo
Topic 15: olympics, medal, tokyo, olympic, medals
Topic 16: sports, hockey, world, city, indian
Topic 17: dhyan, chand, award, ratna, khel
Topic 18: bajrang, medal, punia, wrestler, bronze
Topic 19: india, penalty, team, goal, first
Topic 20: singh, hockey, medal, india, team
Topic 21: minister, government, house, chief, chanu
Topic 22: chopra, army, neeraj, olympics, gold
Topic 23: team, hockey, indian, india, women's
Topic 24: gold, medal, olympics, tokyo, won
Topic 25: rs, cash, crore, announced, lakh
Topic 26: sindhu, pv, medal, olympics, win
Topic 27: mental, bjp, takes, body, congress
Topic 28: world, sindhu, like, olympics, match
Topic 29: men's, women's, ist, team, air
Topic 30: medal, congratulations, proud, india, olympics
Topic 31: team, hockey, family, vandana, caste
Topic 32: sports, games, athletes, olympic, world
Topic 33: team, india, sreejesh, medal, indian
Topic 34: proud, biles, film, abhimanyu, right
Topic 35: medal, first, games, olympics, chanu
Topic 36: brands, brand, pizza, chanu, domino's
Topic 37: athletes, olympics, games, sports, tokyo
Topic 38: coach, world, pistol, olympics, shooters
Topic 39: dahiya, village, medal, gold, world
Topic 40: dahiya, kumar, medal, olympic, wrestler
Topic 41: olympics, tokyo, ceremony, games, athletes
Topic 42: hockey, team, singh, medal, players
Topic 43: sindhu, world, indian, game, tai
Topic 44: hockey, team, family, coach, game
Topic 45: world, athletes, like, olympic, chand
Topic 46: india, chanu, indian, medal, olympics
Topic 47: gold, medal, olympics, olympic, people
Topic 48: mirabai, medal, chanu, silver, olympics
Topic 49: aditi, ashok, round, golf, medal
Topic 50: indian, students, tokyo, olympics, singh
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Completing Iteration 31 (approx. per word bound = -6.801, relative change = 1.664e-05)
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Completing Iteration 35 (approx. per word bound = -6.801, relative change = 2.484e-05)
Topic 1: manika, singles, round, tennis, game
Topic 2: throw, neeraj, gold, javelin, chopra
Topic 3: medal, tokyo, chanu, one, india's
Topic 4: neeraj, gold, chopra, medal, olympics
Topic 5: hockey, india, team, match, medal
Topic 6: women, olympics, india, men, first
Topic 7: team, indian, round, men's, das
Topic 8: hockey, team, odisha, women's, players
Topic 9: sindhu, medal, bronze, olympics, indian
Topic 10: hockey, match, team, women's, india
Topic 11: lovlina, borgohain, medal, boxing, boxer
Topic 12: olympics, like, stories, watch, book
Topic 13: sharma, olympic, back, made, medal
Topic 14: osaka, world, first, gold, tokyo
Topic 15: olympics, medal, tokyo, olympic, games
Topic 16: sports, hockey, world, city, indian
Topic 17: dhyan, chand, award, ratna, khel
Topic 18: bajrang, medal, punia, wrestler, bronze
Topic 19: india, penalty, team, goal, first
Topic 20: singh, hockey, medal, india, team
Topic 21: minister, government, house, chief, chanu
Topic 22: chopra, army, neeraj, olympics, gold
Topic 23: team, hockey, indian, india, women's
Topic 24: gold, medal, tokyo, won, olympics
Topic 25: rs, cash, crore, announced, lakh
Topic 26: sindhu, pv, medal, olympics, win
Topic 27: mental, bjp, takes, body, congress
Topic 28: world, sindhu, like, olympics, match
Topic 29: men's, women's, ist, team, air
Topic 30: medal, congratulations, proud, india, olympics
Topic 31: team, hockey, family, vandana, caste
Topic 32: sports, games, athletes, olympic, world
Topic 33: team, india, sreejesh, medal, indian
Topic 34: proud, biles, film, abhimanyu, right
Topic 35: medal, first, games, olympics, chanu
Topic 36: brands, brand, pizza, chanu, domino's
Topic 37: athletes, olympics, games, sports, tokyo
Topic 38: coach, world, pistol, olympics, shooters
Topic 39: dahiya, village, medal, gold, world
Topic 40: dahiya, kumar, medal, olympic, wrestling
Topic 41: olympics, tokyo, ceremony, games, athletes
Topic 42: hockey, team, singh, medal, players
Topic 43: sindhu, world, indian, game, tai
Topic 44: hockey, team, family, coach, game
Topic 45: world, athletes, like, olympic, chand
Topic 46: india, chanu, indian, medal, olympics
Topic 47: gold, medal, olympics, olympic, people
Topic 48: mirabai, medal, chanu, silver, olympics
Topic 49: aditi, ashok, round, golf, medal
Topic 50: indian, students, tokyo, olympics, singh
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Completing Iteration 40 (approx. per word bound = -6.800, relative change = 1.651e-05)
Topic 1: manika, singles, round, tennis, table
Topic 2: throw, neeraj, gold, javelin, chopra
Topic 3: medal, tokyo, chanu, one, india's
Topic 4: neeraj, gold, chopra, medal, olympics
Topic 5: hockey, india, team, medal, match
Topic 6: women, olympics, india, men, first
Topic 7: team, round, indian, das, men's
Topic 8: hockey, team, odisha, women's, players
Topic 9: sindhu, medal, bronze, olympics, indian
Topic 10: hockey, match, team, women's, india
Topic 11: lovlina, borgohain, medal, boxing, boxer
Topic 12: olympics, like, stories, watch, book
Topic 13: sharma, olympic, back, made, medal
Topic 14: osaka, world, first, gold, tokyo
Topic 15: olympics, medal, tokyo, olympic, games
Topic 16: sports, hockey, world, city, indian
Topic 17: dhyan, chand, award, ratna, khel
Topic 18: bajrang, medal, punia, wrestler, bronze
Topic 19: india, penalty, team, goal, first
Topic 20: singh, hockey, medal, india, team
Topic 21: minister, government, house, chief, chanu
Topic 22: chopra, army, neeraj, olympics, gold
Topic 23: team, hockey, indian, india, women's
Topic 24: gold, medal, tokyo, won, olympics
Topic 25: rs, cash, crore, announced, lakh
Topic 26: sindhu, pv, medal, olympics, win
Topic 27: mental, bjp, takes, body, congress
Topic 28: world, sindhu, like, olympics, time
Topic 29: men's, women's, ist, team, air
Topic 30: medal, congratulations, proud, india, indian
Topic 31: team, hockey, family, vandana, caste
Topic 32: sports, games, athletes, olympic, world
Topic 33: team, india, sreejesh, medal, indian
Topic 34: proud, biles, film, abhimanyu, right
Topic 35: medal, first, games, olympics, chanu
Topic 36: brands, brand, pizza, chanu, domino's
Topic 37: athletes, olympics, games, sports, tokyo
Topic 38: coach, world, pistol, shooters, olympics
Topic 39: dahiya, village, medal, gold, world
Topic 40: dahiya, kumar, medal, olympic, wrestling
Topic 41: olympics, tokyo, ceremony, games, athletes
Topic 42: hockey, team, singh, medal, players
Topic 43: sindhu, world, indian, game, tai
Topic 44: hockey, team, family, coach, game
Topic 45: world, athletes, like, olympic, chand
Topic 46: india, chanu, indian, medal, olympics
Topic 47: gold, medal, olympics, olympic, people
Topic 48: mirabai, medal, chanu, silver, olympics
Topic 49: aditi, ashok, round, golf, medal
Topic 50: indian, students, tokyo, olympics, singh
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Completed E-Step (0 seconds).
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Model Converged plot(differentKs)
Fitting the Latent Dirichlet Allocation topic model for 25 topics
At first I plotted the top 10 words for each topic, however I thought that having the top 20 words would give a better idea of the content covered. ## 25 topics Many of the topics (2,5,8,12,18,20) focus on hockey. However, different aspects pertaining to the game are discussed.
Topic 2 discusses the Prime Minister honouring the hockey players with the highest sports award of India- the Major Dhyan Chand Khel Ratna.
Topic 5 is a little unclear as it has information about marketing but also mentions the police.
Topic 8 covers the details of the hockey games and mentions the other countries that took part in the semifinal and final mens’ matches.
Topic 12 can be considered as the reaction of the public to the game and content of tweets regarding the same.
Topic 18 has a mix of the hockey team’s achievements and PV Sindhu’s (badminton player) achievement Topic 20 discusses how the government of the state of Odisha reacted to the win and stated it would continue to sponsor the women’s and men’s hockey teams.
All of the players mentioned in the topic models were either winners or in the final rounds of their respective sports. They also contained information regarding tweets and prominent celebrities that congratulated them. There was no noticeable difference in the top terms used for male sports players and female sports players.However, one difference that can be observed is that although both the mens’ and womens’ Indian hockey teams played well, majority of the topics were regarding the mens’ achievements (2,8,12 and 18). Perhaps, because they placed third whereas the womens’ team placed fourth.
Similar to hockey, the sports of javelin throw, wrestling and weightlifting were mentioned in several topics. For the gold medallist Neeraj Chopra, there was also a topic which pertained to his army background. Finally, many of the topics about the winners mention cash prizes from sources such as the government and the term ‘rs’ which stands for rupees which is the Indian currency.
The topic that was popular outside the Indian context (topics 1 and 15), regarding the gymnast Simon Biles and the importance of mental health as she had withdrawn from the Olympics due to mental health concerns.
news_lda25 <- LDA(news_dtm, k = 25, control = list(seed = 2345))
news_lda25A LDA_VEM topic model with 25 topics.#extracting per-topic-per-word probabilities
news_topics25 <- tidy(news_lda25, matrix = "beta")
news_topics25# A tibble: 559,125 × 3
topic term beta
<int> <chr> <dbl>
1 1 solitary 3.47e- 25
2 2 solitary 5.65e-233
3 3 solitary 4.77e-232
4 4 solitary 4.00e-231
5 5 solitary 9.70e-232
6 6 solitary 2.56e-231
7 7 solitary 2.56e-233
8 8 solitary 2.46e-231
9 9 solitary 2.52e- 4
10 10 solitary 8.94e- 34
# … with 559,115 more rows#Finding the top 10 terms
news_top_10_25 <- news_topics25 %>%
group_by(topic) %>%
slice_max(beta, n = 10) %>%
ungroup() %>%
arrange(topic, -beta)
news_top_10_25%>%
mutate(term = reorder_within(term, beta, topic)) %>%
ggplot(aes(beta, term, fill = factor(topic))) +
geom_col(show.legend = FALSE) +
facet_wrap(~ topic, scales = "free") +
scale_y_reordered()
#Finding top 20 terms
news_top_20_25 <- news_topics25 %>%
group_by(topic) %>%
slice_max(beta, n = 20) %>%
ungroup() %>%
arrange(topic, -beta)
news_top_20_25%>%
mutate(term = reorder_within(term, beta, topic)) %>%
ggplot(aes(beta, term, fill = factor(topic))) +
geom_col(show.legend = FALSE) +
facet_wrap(~ topic, scales = "free") +
scale_y_reordered()
Since many of the topic models had their top terms as “olympics” or “India”, I wanted to check whether removing these terms would offer a deeper insight into the topics.
#removing some of the common words and then seeing how the topic model looks
articles_dfm_common_rem <- dfm_remove(articles_dfm, c("olympics","olympic","india","indian","tokyo","sports","#tokyo2020","2020","2021","india's"), verbose = TRUE)removed 10 featuresarticles_tidy2<-tidy(articles_dfm_common_rem)
articles_tidy2# A tibble: 195,993 × 3
document term count
<chr> <chr> <dbl>
1 text1 solitary 1
2 text214 solitary 1
3 text245 solitary 1
4 text629 solitary 1
5 text639 solitary 1
6 text797 solitary 1
7 text1099 solitary 1
8 text1 two-day 1
9 text311 two-day 1
10 text368 two-day 1
# … with 195,983 more rowsnews_dtm2<- articles_tidy2 %>%
cast_dtm(document, term, count)
news_dtm2<<DocumentTermMatrix (documents: 1157, terms: 22355)>>
Non-/sparse entries: 195993/25668742
Sparsity : 99%
Maximal term length: 84
Weighting : term frequency (tf)Topic Models with some common words removed
Most of the topics regarding the prominent sports players remained the same.
This model did make it more clear as to why the word police occured in one of the topics that was about hockey. Topic 15 in the model includes words such as casteist, women’s and hockey which refers to the incident where casteist remarks about women hockey players were made after the women’s team had lost a semifinal.
Moreover, Topic 24 has information that is not related to the Olympics at all, which may indicate that some of the news articles in the dataframe could have had multiple headlines being discussed and gotten mixed up with the Olympics news.
news_lda25_remove <- LDA(news_dtm2, k = 25, control = list(seed = 2345))
news_lda25_removeA LDA_VEM topic model with 25 topics.#extracting per-topic-per-word probabilities
news_topics25_remove <- tidy(news_lda25_remove, matrix = "beta")
news_topics25_remove# A tibble: 558,875 × 3
topic term beta
<int> <chr> <dbl>
1 1 solitary 8.63e- 5
2 2 solitary 2.06e- 4
3 3 solitary 6.96e-74
4 4 solitary 6.74e- 5
5 5 solitary 3.72e-74
6 6 solitary 9.11e-74
7 7 solitary 4.09e-74
8 8 solitary 4.43e-74
9 9 solitary 7.81e-74
10 10 solitary 3.93e-74
# … with 558,865 more rows#Finding the top 20 terms
news_top_20_25_remove <- news_topics25_remove %>%
group_by(topic) %>%
slice_max(beta, n = 20) %>%
ungroup() %>%
arrange(topic, -beta)
news_top_20_25_remove%>%
mutate(term = reorder_within(term, beta, topic)) %>%
ggplot(aes(beta, term, fill = factor(topic))) +
geom_col(show.legend = FALSE) +
facet_wrap(~ topic, scales = "free") +
scale_y_reordered()
Greatest difference between 2 topics
In this LDA model, for the topics regarding hockey, the different aspects covered in each are observable. However, for the topics about Neeraj Chopra in the javelin throw event- topics 11 and 25, it is not clear as to the difference in the 2 topics. Hence, I wanted to check for the words which have the greatest difference between the 2 topics. I kept the beta value greater than 1/5000.
In topic 11, the common words seem to be more general such as the act of winning and being in the finals, whereas in topic 25, the common words are more specific to Neeraj Chopra and how his winning was a historical moment in Indian sports.
#Different Neeraj Chopra topics
#beta_11_12%>%select(topic)%>%n_distinct()
beta_11_25<- news_topics25_remove %>%
mutate(topic = paste0("topic", topic))%>%
filter(topic=="topic11"|topic=="topic25")%>%
pivot_wider(names_from =topic, values_from = beta)%>%
filter(topic11 > .005| topic25 > .005) %>%
mutate(log_ratio = log2(topic25/ topic11))
beta_11_25%>%select(log_ratio)%>%max()[1] 2.164677beta_11_25%>%select(log_ratio)%>%min()[1] -1.883413testt <- ggplot(beta_11_25, aes(x = `term`, y = `log_ratio`, width = .5)) +
geom_bar(position = "dodge", stat = "identity") +
coord_flip()
library(plotly)Warning: package 'plotly' was built under R version 4.2.2
Attaching package: 'plotly'The following object is masked from 'package:ggplot2':
last_plotThe following object is masked from 'package:stats':
filterThe following object is masked from 'package:graphics':
layoutggplotly(testt)In the next post, I plan to implement structural topic models.