Blog Post 5

Blog5

Nayan Jani

Topic Modeling

Author

Nayan Jani

Published

December 9, 2022

Code

library(tidyverse)
library(quanteda)
library(quanteda.textplots)
library(quanteda.textmodels)
library(ggplot2)
library(quanteda.dictionaries)
library(quanteda.sentiment)
library(syuzhet)
library(text2vec)
library(stm)
library(LDAvis)
library(tidytext)
library(igraph)
library(textdata)


knitr::opts_chunk$set(echo = TRUE)

Introduction

The FIFA 2022 World Cup has captivated almost everyone’s attention this year. However, most of that attention has been focused on human rights violations that are present. The Host nation Qatar has been under pressure for these violations because of their treatment of foreigners in their country and their failure to be inclusive. Migrant workers that have helped build stadiums have been mistreated, underpaid, overworked and even killed leading up to the World Cup. The Host nation also has disallowed for the LGBTQ community to represent themselves because of the Host nations beliefs. Soccer Fans from both cultures (Host nation vs Foreigners) have argued over what values to respect on the global level.

Objectives

Find the overall Sentiment of the comments (Positive and Negative, Other emotions)

What is the main focus in the comments (what topic is most important to the people in the comments)

Based on the most important topics and the sentiment of those comments, are those comments classified correctly positive or negative? If yes, what are the comments POV? (western culture vs middle east culture) Are those comments “socially correct”? (logical/acceptable POV vs Stereotyped/Stigmatized POV)

Read in Data

Code

df_bbc<- read_csv("_data/comments_bbc.csv")

df_bbc<- df_bbc%>% 
  rename(text = "i")

df_bbc<- df_bbc %>%
  select(text) %>% 
  mutate(text = str_remove_all(text,"39"))

df_bbc <- df_bbc %>% 
  select(text) %>% 
  mutate(text = str_remove_all(text,"<")) %>% 
  mutate(text = str_remove_all(text,">"))

corpus_bbc <- corpus(df_bbc)
corpus_bbc_summary <- summary(corpus_bbc)

corpus_bbc_summary$video <- "BBC"
docvars(corpus_bbc) <- corpus_bbc_summary

df_q<- read_csv("_data/comments_q.csv")

df_q<- df_q %>% 
  rename(text = "I’ll try to get the next video essay out in less than a month lol")

df_q<- df_q %>%
  select(text) %>% 
  mutate(text = str_remove_all(text,"39"))


df_q <- df_q %>% 
  select(text) %>% 
  mutate(text = str_remove_all(text,"<")) %>% 
  mutate(text = str_remove_all(text,">"))

corpus_q <- corpus(df_q)
corpus_q_summary <- summary(corpus_q)
corpus_q_summary$video <- "Maqwell"
docvars(corpus_q) <- corpus_q_summary

df_qRev<- read_csv("_data/comments_qRev.csv")

df_qRev <- df_qRev%>% 
  rename(text = "I’ll try to get the next video essay out in less than a month lol")

df_qRev<- df_qRev %>%
  select(text) %>% 
  mutate(text = str_remove_all(text,"39"))

df_qRev <- df_qRev %>% 
  select(text) %>% 
  mutate(text = str_remove_all(text,"<")) %>% 
  mutate(text = str_remove_all(text,">"))

corpus_qRev <- corpus(df_qRev)
corpus_qRev_summary <- summary(corpus_qRev)
corpus_qRev_summary$video <- "MaqwellRev"
docvars(corpus_qRev) <- corpus_qRev_summary

df_sky<- read_csv("_data/comments_sky.csv")

df_sky<- df_sky%>% 
  rename(text = "i")

df_sky<- df_sky %>%
  select(text) %>% 
  mutate(text = str_remove_all(text,"39"))

df_sky <- df_sky %>% 
  select(text) %>% 
  mutate(text = str_remove_all(text,"<")) %>% 
  mutate(text = str_remove_all(text,">"))

corpus_sky <- corpus(df_sky)
corpus_sky_summary <- summary(corpus_sky)
corpus_sky_summary$video <- "sky"
docvars(corpus_sky) <- corpus_sky_summary


df_bbcQ <- read_csv("_data/comments_bbcQ.csv")


df_bbcQ<- df_bbcQ%>% 
  rename(text = "i")

df_bbcQ<- df_bbcQ %>%
  select(text) %>% 
  mutate(text = str_remove_all(text,"39"))

df_bbcQ <- df_bbcQ %>% 
  select(text) %>% 
  mutate(text = str_remove_all(text,"<")) %>% 
  mutate(text = str_remove_all(text,">"))

corpus_bbcQ<- corpus(df_bbcQ)
corpus_bbcQ_summary <- summary(corpus_bbcQ)
corpus_bbcQ_summary$video <- "BBC"

df_bbcOL <- read_csv("_data/comments_bbcOL.csv")


df_bbcOL<- df_bbcOL%>% 
  rename(text = "i")

df_bbcOL<- df_bbcOL %>%
  select(text) %>% 
  mutate(text = str_remove_all(text,"39"))

df_bbcOL <- df_bbcOL %>% 
  select(text) %>% 
  mutate(text = str_remove_all(text,"<")) %>% 
  mutate(text = str_remove_all(text,">"))

corpus_bbcOL<- corpus(df_bbcOL)
corpus_bbcOL_summary <- summary(corpus_bbcOL)
corpus_bbcOL_summary$video <- "BBC"


df_BI <- read_csv("_data/comments_BI.csv")


df_BI<- df_BI%>% 
  rename(text = "i")

df_BI<- df_BI %>%
  select(text) %>% 
  mutate(text = str_remove_all(text,"39"))

df_BI <- df_BI %>% 
  select(text) %>% 
  mutate(text = str_remove_all(text,"<")) %>% 
  mutate(text = str_remove_all(text,">"))

corpus_BI<- corpus(df_BI)
corpus_BI_summary <- summary(corpus_BI)
corpus_BI_summary$video <- "Business Insider"

df_fra <- read_csv("_data/comments_fra.csv")


df_fra<- df_fra%>% 
  rename(text = "i")

df_fra<- df_fra %>%
  select(text) %>% 
  mutate(text = str_remove_all(text,"39"))

df_fra <- df_fra %>% 
  select(text) %>% 
  mutate(text = str_remove_all(text,"<")) %>% 
  mutate(text = str_remove_all(text,">"))

corpus_fra<- corpus(df_fra)
corpus_fra_summary <- summary(corpus_fra)
corpus_fra_summary$video <- "France 21"

df_H <- read_csv("_data/comments_H.csv")


df_H<- df_H%>% 
  rename(text = "i")

df_H<- df_H %>%
  select(text) %>% 
  mutate(text = str_remove_all(text,"39"))

df_H <- df_H %>% 
  select(text) %>% 
  mutate(text = str_remove_all(text,"<")) %>% 
  mutate(text = str_remove_all(text,">"))

corpus_H<- corpus(df_H)
corpus_H_summary <- summary(corpus_H)
corpus_H_summary$video <- "Harris"

Code

full_df <- rbind(df_bbc,df_q,df_qRev,df_sky,df_bbcQ,df_bbcOL,df_BI,df_fra,df_H)
full_df$id <- 1:nrow(full_df)
full_df$id <- as.character(full_df$id)

head(full_df)

# A tibble: 6 × 2
  text                                                                     id   
  <chr>                                                                    <chr>
1 Looking forward to it if you just act normal respect the culture and co… 1    
2 Honestly every country in the world has done bad. May Allah bless these… 2    
3 if you don't like it stay home and the last people talking about human … 3    
4 So we can’t boycott a football game ( a trivial matter)  to protest a r… 4    
5 I see everyone kept their mouth shut with Russia&#;s world cup           5    
6 Did Qatar invade any country and kill millions? Who are robbing Africa?… 6

Code

full_corpus <- corpus(full_df$text)
full_corpus_summary<- summary(full_corpus)

Code

tokens1 <- tolower(full_df$text)

# performs tokenization
tokens1 <- word_tokenizer(tokens1,pos_remove = c("PUNCT", "DET", "ADP", "SYM", "PART", "AUX" ))

head(tokens1, 2)

[[1]]
 [1] "looking"    "forward"    "to"         "it"         "if"        
 [6] "you"        "just"       "act"        "normal"     "respect"   
[11] "the"        "culture"    "and"        "country"    "and"       
[16] "you"        "will"       "be"         "ok"         "no"        
[21] "one"        "needs"      "to"         "know"       "someone"   
[26] "is"         "gay"        "just"       "like"       "no"        
[31] "one"        "needs"      "to"         "know"       "if"        
[36] "you"        "hetero"     "just"       "save"       "that"      
[41] "for"        "behind"     "closed"     "doors"      "and"       
[46] "make"       "sure"       "it"         "s"          "between"   
[51] "consenting" "adults"     "children"   "and"        "animals"   
[56] "are"        "not"        "consenting" "adults"     "btw"       
[61] "you"        "perverts"  

[[2]]
 [1] "honestly"  "every"     "country"   "in"        "the"       "world"    
 [7] "has"       "done"      "bad"       "may"       "allah"     "bless"    
[13] "these"     "workers"   "who"       "have"      "done"      "so"       
[19] "much"      "for"       "the"       "country"   "however"   "to"       
[25] "call"      "it"        "slavery"   "is"        "too"       "far"      
[31] "they"      "r"         "not"       "taking"    "them"      "without"  
[37] "there"     "will"      "like"      "when"      "british"   "and"      
[43] "americans" "killed"    "and"       "took"      "thousands" "of"       
[49] "slaves"    "they"      "come"      "here"      "for"       "better"   
[55] "options"   "of"        "course"    "it"        "can"       "be"       
[61] "better"    "however"   "it"        "is"        "much"      "better"   
[67] "than"      "what"      "was"       "happening" "to"        "them"     
[73] "in"        "their"     "own"       "countries"

Code

full_tokens <- tokens(full_corpus,
    remove_numbers = T,
    remove_url = T,
    remove_punct = T,
    remove_symbols = T)
full_tokens <-tokens_tolower(full_tokens)
full_tokens <- tokens_select(full_tokens, 
                              pattern = c(stopwords("en"),"quot","href","don"),
                              selection = "remove",
                              min_nchar = 3)


head(full_tokens, 2)

Tokens consisting of 2 documents.
text1 :
 [1] "looking" "forward" "just"    "act"     "normal"  "respect" "culture"
 [8] "country" "one"     "needs"   "know"    "someone"
[ ... and 22 more ]

text2 :
 [1] "honestly" "every"    "country"  "world"    "done"     "bad"     
 [7] "may"      "allah"    "bless"    "workers"  "done"     "much"    
[ ... and 25 more ]

Topic Model

For LDA topic Modeling, I used the package text2vec. I first wanted to find the best value for K and then extract the top 10 words from my topics.

Code

it1 <- itoken(tokens1, ids = full_df$id, progressbar = FALSE)

stop_words1 = c("i", "me", "my", "myself", "we", "our", "ours", "ourselves", "you", "your", "yours","a","the","in","as","on", "is","it", "to","of","are","not","and","quot","don","youtu.be","an","have","this","if","they","v","2","their","can", "than")
v1 <- create_vocabulary(it1,stopwords = stop_words1)



#v1 <- prune_vocabulary(v1, term_count_min = 5)



vectorizer1 <- vocab_vectorizer(v1)

dtm1 <- create_dtm(it1, vectorizer1, type = "dgTMatrix")

lda_model1 <- LDA$new(n_topics = 5, doc_topic_prior = 0.1,
                     topic_word_prior = 0.01)


doc_topic_distr1 <- 
  lda_model1$fit_transform(x = dtm1, n_iter = 1000,
                          convergence_tol = 0.001, n_check_convergence = 25,
                          progressbar = FALSE)

INFO  [14:58:37.340] early stopping at 125 iteration
INFO  [14:58:37.889] early stopping at 75 iteration

Code

lda_model1$get_top_words(n = 10,
                        lambda = 0.4)

      [,1]      [,2]        [,3]    [,4]       [,5]          
 [1,] "feel"    "east"      "cup"   "respect"  "workers"     
 [2,] "people"  "middle"    "world" "or"       "for"         
 [3,] "way"     "was"       "will"  "there"    "he"          
 [4,] "still"   "lot"       "be"    "when"     "working"     
 [5,] "all"     "but"       "watch" "culture"  "conditions"  
 [6,] "rules"   "were"      "https" "west"     "these"       
 [7,] "saudi"   "man"       "2022"  "laws"     "from"        
 [8,] "company" "even"      "href"  "done"     "construction"
 [9,] "left"    "countries" "fifa"  "football" "lost"        
[10,] "am"      "qatari"    "host"  "no"       "migrant"

The Topic Model implies that the major topic of discussion surrounds showing respect to both cultures (Western and Middle East). The other topics in the model suggests discussions surrounding migrant workers and how they are treated.

Sentiment Analysis

For Sentiment Analysis I used the packages tidytext and sentimentr. Sentimentr attempts to take into account valence shifters (i.e., negators, amplifiers (intensifiers), de-amplifiers (downtoners), and adversative conjunctions). This will give me better results than before.

Code

library(sentimentr)

Warning: package 'sentimentr' was built under R version 4.2.2


Attaching package: 'sentimentr'

The following object is masked from 'package:syuzhet':

    get_sentences

Code

mytext <- get_sentences(full_df$text)
senti<- sentiment_by(mytext)

senti<- senti %>% 
  filter(word_count > 4)

qplot(senti$ave_sentiment,   geom="histogram",binwidth=0.1,main="Sentiment Histogram")

Warning: `qplot()` was deprecated in ggplot2 3.4.0.

Code

writeLines(head(full_corpus[which(senti$ave_sentiment >.5)]))

a country that has modern day slavery, human rights abuses, sharia law which is extremely discriminatory to women and people of any other religion other than Islam and Christianity, one of the highest death rates of workers, yet it&#;s okay to play football there, amazing isn&#;t it, people say money can&#;t buy you everything, show them this shitshow
This Arabic nation is little humanitarian concepts among workers , especially  home maid workers conditions are very brutal no human rights and laws. like fisherman attitude.
A golden opportunity to combat negative stereotypes... used to make them worse.
Chad Qatar : Bribes Virgin West to submission. Humiliaties the West. Doesn&#;t care about stupid lgbt flag. Profits
now do the same when US host a worldwide event lol
Im glad someone made a vid about this, I lived in Doha for 8 years and recently left back to my country. Whenever I would go home from school you could see workers being forced to work in the summers peak temp hour even though it was made illegal to work from 12pm to 4pm or something like that? Theres also a huge lack of safety, cranes with cargo would be moved OVER MOVING TRAFFIC:brbrTo any adults who plan to go to the world cup I will tell you now that many places in Qatar will not serve alchoholic drinks because they need a license and can only purchase their alchohol from QDC. So if you do want alchohol I&#;d reccommend going to a hotel like the Grand Hyatt or something. So don&#;t get upset if ya cant find a place to get beer. (but in the stadium there  will be alchohol drinking zones).

The overall sentiment of the corpus is skewed right, suggesting that most of the comments are negative. The most positive comments show more western culture beliefs and criticisms of Qatar. The reasoning and language the commenters use are socially acceptable based on their knowledge of the situation in Qatar and their experiences. Some of the comments are jokes but the main points get across about their beliefs.

Code

set.seed(1245)


full_dfm <- dfm(full_tokens)

smaller_dfm <- dfm_trim(full_dfm, min_termfreq = 2)

Different K’s

Beginning Spectral Initialization 
     Calculating the gram matrix...
     Finding anchor words...
    .....
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    ............................
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Topic 1: workers, work, rights, see, respect 
 Topic 2: countries, one, really, western, every 
 Topic 3: people, just, can, human, fifa 
 Topic 4: world, country, like, good, know 
 Topic 5: qatar, cup, even, like, money 
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 Topic 4: world, country, like, good, know 
 Topic 5: qatar, cup, like, even, money 
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Model Converged 
Beginning Spectral Initialization 
     Calculating the gram matrix...
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Initialization complete.
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Completing Iteration 5 (approx. per word bound = -6.283, relative change = 5.667e-03) 
Topic 1: cup, world, middle, feel, east 
 Topic 2: fifa, huge, workers, big, proud 
 Topic 3: see, qatar, even, show, stupid 
 Topic 4: people, world, many, just, can 
 Topic 5: world, workers, human, armbands, people 
 Topic 6: world, rights, human, countries, many 
 Topic 7: country, make, back, work, just 
 Topic 8: workers, rights, human, can, like 
 Topic 9: cup, world, football, drink, still 
 Topic 10: love, people, great, video, care 
 Topic 11: like, people, countries, world, rights 
 Topic 12: people, poor, can, workers, life 
 Topic 13: west, people, human, rights, world 
 Topic 14: qatar, years, just, shame, gulf 
 Topic 15: people, country, one, even, like 
 Topic 16: people, world, cup, one, country 
 Topic 17: qatar, respect, well, like, made 
 Topic 18: countries, qatar, like, just, india 
 Topic 19: laws, countries, country, india, sad 
 Topic 20: way, country, world, people, just 
 Topic 21: qatar, workers, world, cup, migrant 
 Topic 22: million, must, war, world, one 
 Topic 23: money, thing, good, anything, workers 
 Topic 24: football, country, culture, people, just 
 Topic 25: mentioned, cup, world, workers, muslim 
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Completing Iteration 6 (approx. per word bound = -6.263, relative change = 3.083e-03) 
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Completing Iteration 7 (approx. per word bound = -6.252, relative change = 1.811e-03) 
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Completing Iteration 8 (approx. per word bound = -6.245, relative change = 1.162e-03) 
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Completing Iteration 9 (approx. per word bound = -6.241, relative change = 6.260e-04) 
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Completing Iteration 10 (approx. per word bound = -6.238, relative change = 4.811e-04) 
Topic 1: cup, world, middle, feel, east 
 Topic 2: fifa, huge, big, event, workers 
 Topic 3: see, qatar, even, stupid, like 
 Topic 4: people, world, just, many, can 
 Topic 5: world, workers, human, armbands, please 
 Topic 6: world, rights, human, countries, many 
 Topic 7: country, make, work, back, get 
 Topic 8: workers, rights, human, like, can 
 Topic 9: cup, football, world, drink, still 
 Topic 10: love, people, great, video, man 
 Topic 11: like, people, countries, human, rights 
 Topic 12: people, can, life, poor, workers 
 Topic 13: west, human, people, rights, world 
 Topic 14: qatar, years, just, shame, gulf 
 Topic 15: people, country, even, one, american 
 Topic 16: world, cup, people, western, country 
 Topic 17: well, qatar, respect, like, just 
 Topic 18: countries, qatar, like, india, job 
 Topic 19: laws, people, country, countries, just 
 Topic 20: way, world, country, people, stadiums 
 Topic 21: qatar, world, cup, workers, migrant 
 Topic 22: million, world, war, must, one 
 Topic 23: money, good, thing, bbc, anything 
 Topic 24: football, country, culture, respect, just 
 Topic 25: mentioned, companies, cup, world, muslim 
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Completing Iteration 11 (approx. per word bound = -6.235, relative change = 4.787e-04) 
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Completing Iteration 12 (approx. per word bound = -6.232, relative change = 4.498e-04) 
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Completing Iteration 13 (approx. per word bound = -6.230, relative change = 3.278e-04) 
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Completing Iteration 14 (approx. per word bound = -6.228, relative change = 3.218e-04) 
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Completing Iteration 15 (approx. per word bound = -6.227, relative change = 1.720e-04) 
Topic 1: cup, world, middle, feel, east 
 Topic 2: fifa, big, huge, event, proud 
 Topic 3: see, qatar, stupid, even, like 
 Topic 4: people, world, just, many, countries 
 Topic 5: world, workers, human, armbands, please 
 Topic 6: world, rights, human, countries, many 
 Topic 7: country, work, make, back, home 
 Topic 8: workers, rights, human, like, can 
 Topic 9: football, cup, drink, world, still 
 Topic 10: love, people, great, video, man 
 Topic 11: like, people, countries, human, rights 
 Topic 12: people, can, life, poor, workers 
 Topic 13: west, human, rights, people, world 
 Topic 14: qatar, years, just, shame, sad 
 Topic 15: people, country, even, one, american 
 Topic 16: world, cup, people, western, country 
 Topic 17: well, qatar, like, work, videos 
 Topic 18: countries, qatar, like, lives, india 
 Topic 19: laws, people, country, india, can 
 Topic 20: way, world, stadiums, people, country 
 Topic 21: world, cup, qatar, workers, migrant 
 Topic 22: million, world, war, must, organization 
 Topic 23: good, money, bbc, thing, god 
 Topic 24: football, country, respect, culture, just 
 Topic 25: mentioned, western, muslim, companies, human 
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Completing Iteration 16 (approx. per word bound = -6.226, relative change = 1.936e-04) 
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Completing Iteration 17 (approx. per word bound = -6.224, relative change = 2.387e-04) 
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Completing Iteration 18 (approx. per word bound = -6.223, relative change = 1.979e-04) 
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Completing Iteration 19 (approx. per word bound = -6.222, relative change = 1.007e-04) 
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Completing Iteration 20 (approx. per word bound = -6.222, relative change = 5.930e-05) 
Topic 1: cup, world, feel, middle, money 
 Topic 2: fifa, big, huge, event, workers 
 Topic 3: see, qatar, stupid, even, like 
 Topic 4: people, world, just, many, countries 
 Topic 5: world, workers, human, armbands, please 
 Topic 6: world, rights, countries, human, many 
 Topic 7: country, work, back, make, home 
 Topic 8: workers, rights, human, like, can 
 Topic 9: cup, football, world, drink, still 
 Topic 10: love, people, video, great, man 
 Topic 11: like, people, countries, human, rights 
 Topic 12: can, people, east, middle, life 
 Topic 13: west, human, rights, world, people 
 Topic 14: qatar, years, just, sad, shame 
 Topic 15: people, country, even, american, one 
 Topic 16: world, cup, people, western, country 
 Topic 17: well, like, qatar, videos, dont 
 Topic 18: countries, qatar, like, lives, india 
 Topic 19: laws, people, country, can, india 
 Topic 20: way, world, stadiums, people, country 
 Topic 21: world, cup, qatar, workers, migrant 
 Topic 22: million, war, world, must, organization 
 Topic 23: good, money, bbc, thing, god 
 Topic 24: respect, football, country, culture, just 
 Topic 25: labour, mentioned, human, rights, france 
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Completing Iteration 21 (approx. per word bound = -6.222, relative change = 5.713e-05) 
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Completing Iteration 22 (approx. per word bound = -6.221, relative change = 1.311e-04) 
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Completing Iteration 23 (approx. per word bound = -6.220, relative change = 1.262e-04) 
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Completing Iteration 24 (approx. per word bound = -6.219, relative change = 7.845e-05) 
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Completing Iteration 25 (approx. per word bound = -6.219, relative change = 1.112e-04) 
Topic 1: cup, world, feel, middle, money 
 Topic 2: fifa, big, event, huge, workers 
 Topic 3: see, qatar, stupid, even, like 
 Topic 4: people, world, just, many, can 
 Topic 5: workers, world, armbands, human, want 
 Topic 6: world, countries, rights, human, many 
 Topic 7: country, work, back, get, make 
 Topic 8: workers, rights, qatar, human, like 
 Topic 9: cup, world, football, drink, still 
 Topic 10: love, people, video, great, man 
 Topic 11: like, people, countries, human, rights 
 Topic 12: east, middle, can, people, workers 
 Topic 13: west, human, rights, world, people 
 Topic 14: qatar, just, years, now, sad 
 Topic 15: people, country, even, american, one 
 Topic 16: world, cup, western, people, country 
 Topic 17: well, like, qatar, dont, videos 
 Topic 18: countries, qatar, like, lives, india 
 Topic 19: laws, country, people, can, india 
 Topic 20: way, world, stadiums, people, died 
 Topic 21: world, cup, qatar, workers, migrant 
 Topic 22: million, war, world, must, organization 
 Topic 23: good, money, bbc, really, god 
 Topic 24: respect, football, country, culture, just 
 Topic 25: labour, human, rights, mentioned, france 
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Completing Iteration 26 (approx. per word bound = -6.218, relative change = 1.052e-04) 
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Completing Iteration 27 (approx. per word bound = -6.218, relative change = 7.594e-05) 
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Completing Iteration 28 (approx. per word bound = -6.217, relative change = 5.806e-05) 
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Completing Iteration 29 (approx. per word bound = -6.217, relative change = 4.600e-05) 
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Completing Iteration 30 (approx. per word bound = -6.217, relative change = 5.731e-05) 
Topic 1: cup, world, feel, middle, money 
 Topic 2: fifa, big, event, countries, workers 
 Topic 3: see, qatar, stupid, even, like 
 Topic 4: people, world, just, many, can 
 Topic 5: workers, world, armbands, want, please 
 Topic 6: world, countries, rights, human, many 
 Topic 7: country, work, back, get, home 
 Topic 8: workers, qatar, like, many, can 
 Topic 9: cup, world, football, drink, still 
 Topic 10: love, people, video, great, man 
 Topic 11: like, people, countries, can, world 
 Topic 12: middle, east, can, people, workers 
 Topic 13: west, human, world, people, rights 
 Topic 14: qatar, just, years, now, sad 
 Topic 15: people, country, even, american, one 
 Topic 16: world, cup, western, people, one 
 Topic 17: well, like, qatar, dont, videos 
 Topic 18: countries, qatar, like, lives, india 
 Topic 19: laws, country, people, can, india 
 Topic 20: way, world, stadiums, people, died 
 Topic 21: world, cup, qatar, workers, migrant 
 Topic 22: million, war, world, must, organization 
 Topic 23: good, money, bbc, really, thank 
 Topic 24: respect, country, football, culture, just 
 Topic 25: human, rights, labour, mentioned, france 
.........................................................................................................
Completed E-Step (0 seconds). 
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Completing Iteration 31 (approx. per word bound = -6.216, relative change = 4.346e-05) 
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Model Converged 
Beginning Spectral Initialization 
     Calculating the gram matrix...
     Finding anchor words...
    ..................................................
     Recovering initialization...
    ............................
Initialization complete.
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Completing Iteration 1 (approx. per word bound = -7.132) 
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Completing Iteration 2 (approx. per word bound = -6.290, relative change = 1.180e-01) 
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Completing Iteration 3 (approx. per word bound = -6.014, relative change = 4.388e-02) 
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Completing Iteration 4 (approx. per word bound = -5.946, relative change = 1.134e-02) 
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Completing Iteration 5 (approx. per word bound = -5.916, relative change = 4.958e-03) 
Topic 1: international, give, playing, saudi, little 
 Topic 2: fifa, boycott, can, say, proud 
 Topic 3: treated, corrupt, like, people, qatar 
 Topic 4: workers, qatar, like, south, countries 
 Topic 5: please, life, children, make, think 
 Topic 6: world, cup, qatar, fifa, hosting 
 Topic 7: just, qatar, gay, money, pay 
 Topic 8: workers, qatar, qatari, companies, get 
 Topic 9: lgbtq, make, event, soccer, qatar 
 Topic 10: workers, employers, law, qatar, even 
 Topic 11: like, making, strike, side, keep 
 Topic 12: news, conditions, men, area, horrible 
 Topic 13: anything, people, stayed, years, question 
 Topic 14: respect, qatar, support, country, culture 
 Topic 15: heart, നുണ, ibrahim, big, documentary 
 Topic 16: qatar, english, done, look, standard 
 Topic 17: video, well, play, like, sadly 
 Topic 18: people, enough, qatar, like, video 
 Topic 19: religion, people, part, can, evil 
 Topic 20: way, country, stadiums, rights, people 
 Topic 21: laws, can, people, clean, place 
 Topic 22: million, must, world, war, time 
 Topic 23: thing, one, people, just, love 
 Topic 24: qatar, like, came, asia, human 
 Topic 25: mentioned, workers, qatar, country, get 
 Topic 26: getting, world, glad, something, people 
 Topic 27: qatar, european, france, propaganda, take 
 Topic 28: one, love, keep, god, people 
 Topic 29: sad, one, never, family, friend 
 Topic 30: cup, going, tournament, much, workers 
 Topic 31: yes, racism, people, qatar, can 
 Topic 32: india, bbc, media, reason, get 
 Topic 33: great, asian, end, johnny, many 
 Topic 34: workers, shocking, mind, heartbreaking, just 
 Topic 35: hope, europe, work, also, organization 
 Topic 36: huge, qatar, workers, thank, countries 
 Topic 37: feel, people, qatar, can, back 
 Topic 38: western, west, humanity, world, qatar 
 Topic 39: people, canada, poor, qatar, workers 
 Topic 40: country, culture, people, western, nothing 
 Topic 41: west, world, human, workers, even 
 Topic 42: football, politics, know, just, human 
 Topic 43: now, qatar, just, football, money 
 Topic 44: double, hypocrisy, countries, western, standards 
 Topic 45: workers, people, bangladesh, poor, arabs 
 Topic 46: better, living, conditions, qatar, work 
 Topic 47: never, see, human, rights, people 
 Topic 48: slavery, people, muslims, modern, happening 
 Topic 49: countries, qatar, human, western, like 
 Topic 50: sad, breaking, heart, qatar, people 
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Completing Iteration 6 (approx. per word bound = -5.901, relative change = 2.535e-03) 
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Completing Iteration 7 (approx. per word bound = -5.892, relative change = 1.525e-03) 
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Completing Iteration 8 (approx. per word bound = -5.886, relative change = 1.113e-03) 
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Completing Iteration 9 (approx. per word bound = -5.879, relative change = 1.192e-03) 
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Completing Iteration 10 (approx. per word bound = -5.874, relative change = 8.992e-04) 
Topic 1: international, give, playing, saudi, little 
 Topic 2: fifa, can, boycott, say, proud 
 Topic 3: treated, corrupt, like, people, disgraceful 
 Topic 4: workers, qatar, south, like, countries 
 Topic 5: please, life, children, think, make 
 Topic 6: world, cup, qatar, rights, hosting 
 Topic 7: just, gay, qatar, pay, money 
 Topic 8: workers, qatar, qatari, companies, job 
 Topic 9: lgbtq, make, event, soccer, just 
 Topic 10: workers, employers, law, migrant, even 
 Topic 11: like, making, strike, side, labor 
 Topic 12: news, conditions, men, horrible, area 
 Topic 13: anything, people, question, going, stayed 
 Topic 14: qatar, respect, country, support, culture 
 Topic 15: sad, big, heart, watching, people 
 Topic 16: look, make, english, done, qatar 
 Topic 17: well, video, play, like, people 
 Topic 18: people, enough, video, like, qatar 
 Topic 19: religion, people, can, part, things 
 Topic 20: way, country, stadiums, rights, people 
 Topic 21: laws, people, public, can, clean 
 Topic 22: million, must, world, war, time 
 Topic 23: thing, one, love, people, just 
 Topic 24: qatar, like, came, human, asia 
 Topic 25: mentioned, workers, qatar, country, get 
 Topic 26: getting, world, something, glad, people 
 Topic 27: qatar, france, european, propaganda, western 
 Topic 28: love, one, keep, god, people 
 Topic 29: one, never, friend, thank, british 
 Topic 30: going, much, tournament, cup, still 
 Topic 31: yes, racism, people, want, can 
 Topic 32: india, bbc, media, reason, get 
 Topic 33: great, video, asian, end, johnny 
 Topic 34: workers, shocking, mind, just, died 
 Topic 35: hope, europe, work, also, help 
 Topic 36: huge, qatar, workers, thank, countries 
 Topic 37: feel, qatar, people, back, like 
 Topic 38: west, humanity, western, world, human 
 Topic 39: people, canada, poor, workers, qatar 
 Topic 40: country, people, culture, western, nothing 
 Topic 41: west, human, workers, even, problem 
 Topic 42: football, politics, know, sports, human 
 Topic 43: now, qatar, money, football, just 
 Topic 44: double, hypocrisy, countries, western, standards 
 Topic 45: workers, people, poor, bangladesh, arabs 
 Topic 46: better, living, conditions, work, country 
 Topic 47: never, see, human, rights, people 
 Topic 48: slavery, people, muslims, modern, rights 
 Topic 49: countries, qatar, human, like, rights 
 Topic 50: breaking, people, good, qatar, sad 
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Completed E-Step (0 seconds). 
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Completing Iteration 11 (approx. per word bound = -5.871, relative change = 4.543e-04) 
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Completing Iteration 12 (approx. per word bound = -5.870, relative change = 1.828e-04) 
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Completed E-Step (0 seconds). 
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Model Converged

Based on the results of Held-Out Likelihood and Semantic Coherence, I would want to test different K’s between 1-10.

Pairwise Corelation

Here I want to examine correlation among words, which indicates how often they appear together relative to how often they appear separately. The pairwise_cor() function in widyr lets us find the phi coefficient between words based on how often they appear in the same section. Here I pick particular terms of interest and find the other terms most associated with them and create a visualization of the correlations and clusters of words.

Code

section_words <- full_df %>%
  mutate(section = row_number() %/% 10) %>%
  filter(section > 0) %>%
  unnest_tokens(word, text) %>%
  filter(!word %in% stop_words$word) %>% 
  filter(word != "https") %>% 
  filter(word != "href") %>% 
  filter(word != "www.youtube.com") %>%
  filter(word != "youtu.be") %>% 
  filter(word!= "3") %>% 
  filter(word!= "2") %>% 
  filter(word!= "1") %>% 
  filter(word!= "12") %>% 
  filter(word!= "ve")
  
  

library(widyr)

Warning: package 'widyr' was built under R version 4.2.2

Code

library(ggraph)

Warning: package 'ggraph' was built under R version 4.2.2

Code

word_cors <- section_words %>%
  group_by(word) %>%
  filter(n() >= 15) %>%
  pairwise_cor(word, section, sort = TRUE)



set.seed(2016)

word_cors %>%
  filter(correlation > .35) %>%
  graph_from_data_frame() %>%
  ggraph(layout = "fr") +
  geom_edge_link(aes(edge_alpha = correlation), show.legend = FALSE) +
  geom_node_point(color = "lightblue", size = 5) +
  geom_node_text(aes(label = name), repel = TRUE) +
  theme_void()

Warning: Using the `size` aesthetic in this geom was deprecated in ggplot2 3.4.0.
ℹ Please use `linewidth` in the `default_aes` field and elsewhere instead.

Code

word_cors %>%
  filter(item1 %in% c("western", "qatar", "lgbtq", "workers","rights","respect","country","cultures")) %>%
  group_by(item1) %>%
  slice_max(correlation, n = 6) %>%
  ungroup() %>%
  mutate(item2 = reorder(item2, correlation)) %>%
  ggplot(aes(item2, correlation)) +
  geom_bar(stat = "identity") +
  facet_wrap(~ item1, scales = "free") +
  coord_flip()

The Words of interest that I picked include “western”, “qatar”, “lgbtq”, “workers”,“rights”,“respect”,“country”,“cultures”. The correlation between the words respect and culture, politics suggests that respecting the culture and politics of a certain region is being discussed when talking about respect. The Visualization shows the correlation between many words. The relationships here are symmetrical, rather than directional. The connections between words help verify what topics are being discussed in all of the comments. For example, the cluster surrounding the word “worker” shows that the word is correlated with negative terms that relate to the treatment and condition they received.

For my next post, I will put all my work together with my full dataset and continue to fine tune my analysis.

Introduction

Objectives

Read in Data

Topic Model

Sentiment Analysis

Different K’s

Pairwise Corelation

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