Blog Post 5

Code

library(readxl)
library(plyr)
library(tidyverse)
library(tidytext)
library(quanteda)
library(stm)

knitr::opts_chunk$set(echo = TRUE)

In my last post I applied dictionary methods to my corpus: responses to the open-text survey question “What changes would you like to see for Missoula’s food system?” I used both the NRC and General Inquirer dictionaries to conduct a sentiment analysis but ultimately decided that this was not an appropriate analysis for my corpus. This is primarily because the survey question doesn’t ask specifically about respondents’ feelings about anything, meaning that a typical valid response doesn’t contain either positive or negative sentiment.

I plan to create my own dictionary at some point but for this post I will be attempting to analyze my corpus using Structural Topic Modeling (STM). This method of analysis will allow me to understand both the prevalence of topics across my corpus and the content (i.e. words used to talk about) those topics. Importantly, STM will allow me to analyze the responses alongside some covariates of interest. The two that I am particularly interested in are ZIP Code and annual household income. I expect that at least the prevalence of topics will vary by these two covariates, if not the content. Keeping the long term goal of this analysis in mind, it makes a tremendous difference if respondents recommending a particular change to the food system are concentrated in a single ZIP Code or income bracket or distributed across the city/over all income brackets.

Creating My Document Feature Matrix

I created a DFM in a previous post but I’ll create a new one here because the one I previously created doesn’t contain the covariates I’m interested in.

Code

# load tidy data
load("CFA_tidy.RData")

# create dfm
dfm_change <- dfm(tokens(CFA_tidy$change),
             tolower = TRUE,
             remove = stopwords("en"),
             remove_punct = TRUE)

Warning: '...' should not be used for tokens() arguments; use 'tokens()' first.

Warning: 'remove' is deprecated; use dfm_remove() instead

Code

# get dimensions
dim(dfm_change)

[1]  215 1146

It contains 215 rows (the responses, called documents here) and 1,146 columns (all the words that appear in the corpus).

When I first attempted to build a STM, I noticed that the stm() function automatically removed NA values but that that created problems in later functions. I went back to my tidy dataset and removed all rows that contained any NA values to try and address that. Of the 389 respondents, only 215 answered every question.

Now I’ll take a quick look at the two covariates I am interested in: ZIP Code and annual household income.

Code

# zip
table(CFA_tidy$zip)

59701 59801 59802 59803 59804 59808 59821 59823 59825 59826 59833 59846 59847 
    1    85    62    20     9    15     1     5     4     1     2     1     5 
59860 59864 59868 59908 
    1     1     1     1 

Code

# income
table(CFA_tidy$income)

Between $100,000 and $249,999   Between $25,000 and $34,999 
                           55                            21 
  Between $35,000 and $49,999   Between $50,000 and $99,999 
                           37                            66 
                Over $250,000                 Under $25,000 
                            8                            28 

Correlated Topic Model

Code

# build correlated topic model
ctm_change <- stm(dfm_change, K = 5,
                       verbose = FALSE, init.type = "Spectral")

Warning in dfm2stm(x, docvars, omit_empty = TRUE): Dropped empty document(s):
text96

Code

# get summary
summary(ctm_change)

A topic model with 5 topics, 214 documents and a 1146 word dictionary.

Topic 1 Top Words:
     Highest Prob: affordable, local, land, less, gardens, access, farm 
     FREX: less, meals, affordable, land, communities, county, dairy 
     Lift: boulevards, less, meals, 2-5x, accessed, acquisition, additional 
     Score: affordable, land, use, affordability, less, boulevards, raised 
Topic 2 Top Words:
     Highest Prob: local, farmers, market, support, ag, produce, winter 
     FREX: ag, winter, enough, farmer's, living, market, costs 
     Lift: -or, +, 1, 1,000, 10, ability, activities 
     Score: winter, costs, ag, among, evidence-based, feeding, market 
Topic 3 Top Words:
     Highest Prob: food, locally, like, local, see, healthy, can 
     FREX: grown, produced, locally, economically, food, healthy, year 
     Lift: cans, grown, resource, allowing, attend, average, barriers 
     Score: locally, grown, poor, produced, food, can, resource 
Topic 4 Top Words:
     Highest Prob: food, see, school, like, land, local, sustainable 
     FREX: focus, also, space, school, development, help, preserve 
     Lift: $, 5-, abundant, alleviate, allowed, along, also 
     Score: csa, lose, pandemic, space, livestock, ranchers, urban 
Topic 5 Top Words:
     Highest Prob: local, foods, produce, stores, farmers, markets, community 
     FREX: afford, restaurants, away, buying, composting, even, many 
     Lift: #11, adults, alone, appealing, applaud, apple, apples 
     Score: composting, produce, afford, really, buying, shares, balance 

A few things to note:

• It looks like the stm() function automatically drops empty documents. Because I removed all rows that contained NA values from my tidy dataset, however, I am not sure why Text 96 is being considered “empty.” I have a feeling this is going to cause problems later on.
• I specified that k = 5 and while that doesn’t seem unreasonable, I did just make that number up.
• There are packages and functions specifically for building CTM but here I used the stm() function from the stm package because I am ultimately building a STM and not a CTM.

Next I’ll use the labelTopics() function to take a look at the words that are associated with each of the five topics that have been revealed.

Code

# label topics
labelTopics(ctm_change)

Topic 1 Top Words:
     Highest Prob: affordable, local, land, less, gardens, access, farm 
     FREX: less, meals, affordable, land, communities, county, dairy 
     Lift: boulevards, less, meals, 2-5x, accessed, acquisition, additional 
     Score: affordable, land, use, affordability, less, boulevards, raised 
Topic 2 Top Words:
     Highest Prob: local, farmers, market, support, ag, produce, winter 
     FREX: ag, winter, enough, farmer's, living, market, costs 
     Lift: -or, +, 1, 1,000, 10, ability, activities 
     Score: winter, costs, ag, among, evidence-based, feeding, market 
Topic 3 Top Words:
     Highest Prob: food, locally, like, local, see, healthy, can 
     FREX: grown, produced, locally, economically, food, healthy, year 
     Lift: cans, grown, resource, allowing, attend, average, barriers 
     Score: locally, grown, poor, produced, food, can, resource 
Topic 4 Top Words:
     Highest Prob: food, see, school, like, land, local, sustainable 
     FREX: focus, also, space, school, development, help, preserve 
     Lift: $, 5-, abundant, alleviate, allowed, along, also 
     Score: csa, lose, pandemic, space, livestock, ranchers, urban 
Topic 5 Top Words:
     Highest Prob: local, foods, produce, stores, farmers, markets, community 
     FREX: afford, restaurants, away, buying, composting, even, many 
     Lift: #11, adults, alone, appealing, applaud, apple, apples 
     Score: composting, produce, afford, really, buying, shares, balance 

According to the package documentation:

Highest Probability are the words with the highest probability of association with that topic .

FREX weights words by their overall frequency and how exclusive they are to that topic.

Lift weights words by dividing by their frequency in other topics.

Score divides the log frequency of the word in the topic by the log frequency of the word in other topics.

Next the findThoughts() function will allow me to see an example document that is highly associated with each of the five topics.

Code

# find thoughts
findThoughts(ctm_change,
             texts = CFA_tidy$change,
             topics = c(1:5),
             n = 1)

Error in findThoughts(ctm_change, texts = CFA_tidy$change, topics = c(1:5), : Number of provided texts and number of documents modeled do not match

I think that this error is occurring because Text 96 was removed and so now there are 215 rows in my dataset but only 214 texts. As mentioned previously, I am not sure why Text 96 was removed so I’ll take a look at it now and see if I can figure it out.

Code

# get text 96
CFA_tidy[96,]

# A tibble: 1 × 5
  id          zip     age income                      change
  <chr>       <chr> <dbl> <chr>                       <chr> 
1 12981031063 59802    63 Between $50,000 and $99,999 ?     

I see the problem. That row was not removed when I removed rows with NA values but the change cell contains no information in my DFM because I removed punctuation. I’ll remove that row and see if that fixes the problem.

Code

# remove row 96
CFA_tidy_2 <- subset(CFA_tidy, change !="?")

# save
save(CFA_tidy_2, file = "CFA_tidy_2.RData")

# view to check
print(CFA_tidy_2)

# A tibble: 214 × 5
   id          zip     age income                        change                 
   <chr>       <chr> <dbl> <chr>                         <chr>                  
 1 12978141302 59823    72 Under $25,000                 "More concentration on…
 2 12991074702 59802    29 Between $50,000 and $99,999   "I'd like to see more …
 3 12985165265 59801    60 Between $50,000 and $99,999   "Factual, evidence-bas…
 4 12978197761 59801    61 Between $50,000 and $99,999   "Whatever it takes to …
 5 13020467460 59801    83 Between $35,000 and $49,999   "More competitive pric…
 6 12972697121 59802    56 Between $100,000 and $249,999 "More education about …
 7 12977827881 59803    65 Between $50,000 and $99,999   "Greater focus on loca…
 8 13003104091 59808    29 Between $35,000 and $49,999   "I applaud good food s…
 9 12981080680 59801    54 Between $100,000 and $249,999 "More policies and pro…
10 12977865121 59802    27 Under $25,000                 "An expansion of more …
# … with 204 more rows

Everything looks good to me so now I’ll create a new DFM from my new tidy dataset.

Code

# create dfm
dfm_change_2 <- dfm(tokens(CFA_tidy_2$change),
             tolower = TRUE,
             remove = stopwords("en"),
             remove_punct = TRUE)

Warning: '...' should not be used for tokens() arguments; use 'tokens()' first.

Warning: 'remove' is deprecated; use dfm_remove() instead

Code

# save
save(dfm_change_2, file = "dfm_change_2.RData")

# get dimensions
dim(dfm_change_2)

[1]  214 1146

Code

# build correlated topic model
ctm_change_2 <- stm(dfm_change_2, K = 5,
                       verbose = FALSE, init.type = "Spectral")
# get summary
summary(ctm_change_2)

A topic model with 5 topics, 214 documents and a 1146 word dictionary.

Topic 1 Top Words:
     Highest Prob: affordable, local, land, less, gardens, access, farm 
     FREX: less, meals, affordable, land, communities, county, dairy 
     Lift: boulevards, less, meals, 2-5x, accessed, acquisition, additional 
     Score: affordable, land, use, affordability, less, boulevards, raised 
Topic 2 Top Words:
     Highest Prob: local, farmers, market, support, ag, produce, winter 
     FREX: ag, winter, enough, farmer's, living, market, costs 
     Lift: -or, +, 1, 1,000, 10, ability, activities 
     Score: winter, costs, ag, among, evidence-based, feeding, market 
Topic 3 Top Words:
     Highest Prob: food, locally, like, local, see, healthy, can 
     FREX: grown, produced, locally, economically, food, healthy, year 
     Lift: cans, grown, resource, allowing, attend, average, barriers 
     Score: locally, grown, poor, produced, food, can, resource 
Topic 4 Top Words:
     Highest Prob: food, see, school, like, land, local, sustainable 
     FREX: focus, also, space, school, development, help, preserve 
     Lift: $, 5-, abundant, alleviate, allowed, along, also 
     Score: csa, lose, pandemic, space, livestock, ranchers, urban 
Topic 5 Top Words:
     Highest Prob: local, foods, produce, stores, farmers, markets, community 
     FREX: afford, restaurants, away, buying, composting, even, many 
     Lift: #11, adults, alone, appealing, applaud, apple, apples 
     Score: composting, produce, afford, really, buying, shares, balance 

Code

# label topics
labelTopics(ctm_change_2)

Topic 1 Top Words:
     Highest Prob: affordable, local, land, less, gardens, access, farm 
     FREX: less, meals, affordable, land, communities, county, dairy 
     Lift: boulevards, less, meals, 2-5x, accessed, acquisition, additional 
     Score: affordable, land, use, affordability, less, boulevards, raised 
Topic 2 Top Words:
     Highest Prob: local, farmers, market, support, ag, produce, winter 
     FREX: ag, winter, enough, farmer's, living, market, costs 
     Lift: -or, +, 1, 1,000, 10, ability, activities 
     Score: winter, costs, ag, among, evidence-based, feeding, market 
Topic 3 Top Words:
     Highest Prob: food, locally, like, local, see, healthy, can 
     FREX: grown, produced, locally, economically, food, healthy, year 
     Lift: cans, grown, resource, allowing, attend, average, barriers 
     Score: locally, grown, poor, produced, food, can, resource 
Topic 4 Top Words:
     Highest Prob: food, see, school, like, land, local, sustainable 
     FREX: focus, also, space, school, development, help, preserve 
     Lift: $, 5-, abundant, alleviate, allowed, along, also 
     Score: csa, lose, pandemic, space, livestock, ranchers, urban 
Topic 5 Top Words:
     Highest Prob: local, foods, produce, stores, farmers, markets, community 
     FREX: afford, restaurants, away, buying, composting, even, many 
     Lift: #11, adults, alone, appealing, applaud, apple, apples 
     Score: composting, produce, afford, really, buying, shares, balance 

Code

# find thoughts
findThoughts(ctm_change_2,
             texts = CFA_tidy_2$change,
             topics = c(1:5),
             n = 1)

 Topic 1: 
     Community gardens should be a part of all housing developments, the city is stacking people on top of people with no regard for consequences including burying prime agricultural land under a sea of concrete. 
 Topic 2: 
     An expansion of more produce for low income families in aid packages. The boost in farmer’s market coupons and extra produce with wic during summer is amazing but in the winter it’s just back to way too much cereal and grains which isn’t okay. One of our biggest issues in the winter is finding funds for a variety of fresh foods since everything becomes so much more expensive. I’m not vying for pineapples in January or anything but maybe an indoor farmer’s market to get greens and greenhouse crops from local farmers. That could extend the farmer’s season and provide more fresh options to missoulians that haven’t traveled 1,000+ miles 
 Topic 3: 
     I’d like to see I ncreased opportunities for preservation of irrigable lands and increased local food options (although Neva and other folks have done a good job trying). Water is a limited resource, so preservation of that resource (and valuable soils) will largely be what determines our food security into the future. 
 Topic 4: 
     The urban sprawl and development of prime farm land is a big deal. Once a subdivision is built, there is no going back.  It would also be nice if urban farms could legally raise livestock including more than 6 chickens, pigs, cows and goats. Urban farms would be more effficient systems if they were allowed to integrate livestock into their farming systems. For example, grazing animals to keep grass and weeds down, pigs to feed farm/food waste too, and all of the manure to compost and use for fertilizer. 
 Topic 5: 
     More community garden plots in underserved areas with regular on site support to teach families (or even just kids)  to plant, grow and harvest.  Sometimes kids want a better life than their parents are willing or able to provide.

Yes! That seems to have resolved the problem. Now I’ll use the second DFM to build a STM.

Structured Topic Model

Code

# specify number of topics
k <- 5

# specify model
stm_change <- stm(dfm_change_2,
               K = k,
               prevalence = ~ zip,
               data = CFA_tidy_2,
               max.em.its = 1000,
               seed = 1234,
               init.type = "Spectral")

Beginning Spectral Initialization 
     Calculating the gram matrix...
     Finding anchor words...
    .....
     Recovering initialization...
    ...........
Initialization complete.
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Topic 1: land, affordable, access, gardens, less 
 Topic 2: farmers, local, support, can, greater 
 Topic 3: food, like, healthy, available, farmland 
 Topic 4: local, farm, sustainable, missoula, stores 
 Topic 5: see, community, produce, options, foods 
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 Topic 3: food, like, healthy, locally, available 
 Topic 4: local, farm, sustainable, missoula, stores 
 Topic 5: see, community, produce, foods, options 
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Topic 1: land, local, affordable, gardens, community 
 Topic 2: local, farmers, market, support, ag 
 Topic 3: food, locally, like, see, healthy 
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 Topic 2: local, farmers, market, support, ag 
 Topic 3: food, locally, like, see, local 
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 Topic 5: local, foods, produce, stores, food 
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 Topic 2: local, farmers, market, support, ag 
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Topic 1: land, local, affordable, gardens, community 
 Topic 2: local, farmers, market, support, ag 
 Topic 3: food, locally, like, see, local 
 Topic 4: food, see, school, like, sustainable 
 Topic 5: local, food, foods, produce, stores 
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Model Converged 

Code

# label topics
labelTopics(stm_change)

Topic 1 Top Words:
     Highest Prob: land, local, affordable, gardens, community, farm, use 
     FREX: affordability, meals, gardens, land, use, affordable, communities 
     Lift: 2-5x, accessed, acquisition, additional, advocacy, affordability, always 
     Score: land, use, affordable, affordability, agricultural, boulevards, county 
Topic 2 Top Words:
     Highest Prob: local, farmers, market, support, ag, produce, winter 
     FREX: ag, winter, enough, farmer's, living, market, costs 
     Lift: -or, +, 1, 1,000, 10, ability, activities 
     Score: winter, ag, costs, market, climate, farmer's, among 
Topic 3 Top Words:
     Highest Prob: food, locally, like, see, local, can, grown 
     FREX: grown, produced, locally, agriculture, can, food, economically 
     Lift: agriculture, grown, allowing, although, attend, average, barriers 
     Score: locally, grown, produced, can, poor, like, resource 
Topic 4 Top Words:
     Highest Prob: food, see, school, like, sustainable, local, land 
     FREX: also, space, help, school, schools, sustainable, see 
     Lift: $, 5-, 6, abundant, addressing, alleviate, allowed 
     Score: csa, space, 6, livestock, lose, never, pandemic 
Topic 5 Top Words:
     Highest Prob: local, food, foods, produce, stores, markets, farmers 
     FREX: afford, restaurants, away, foods, stores, buying, composting 
     Lift: away, #11, adults, alone, amounts, appealing, applaud 
     Score: composting, produce, buying, afford, really, even, etc 

Code

plot(stm_change, type = "summary")

This is interesting. “Food” appears in all of the topics. This isn’t surprising but I wonder how meaningful its inclusion here is. It might be more interesting to remove it when creating my DFM and see what other words surface once it’s been removed. Given the question I think it’s safe to assume that every topic should have something to do with food.

Code

# estimate effects of income
income_effects <- estimateEffect(formula = 1:k ~ income,
                               stmobj = stm_change,
                               metadata = CFA_tidy_2)

# get summary
summary(income_effects)

Call:
estimateEffect(formula = 1:k ~ income, stmobj = stm_change, metadata = CFA_tidy_2)


Topic 1:

Coefficients:
                                  Estimate Std. Error t value Pr(>|t|)    
(Intercept)                        0.21748    0.03619   6.009 8.24e-09 ***
incomeBetween $25,000 and $34,999  0.01484    0.07135   0.208    0.835    
incomeBetween $35,000 and $49,999 -0.01307    0.06106  -0.214    0.831    
incomeBetween $50,000 and $99,999 -0.02604    0.05093  -0.511    0.610    
incomeOver $250,000               -0.04965    0.09989  -0.497    0.620    
incomeUnder $25,000               -0.02115    0.06156  -0.344    0.731    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1


Topic 2:

Coefficients:
                                   Estimate Std. Error t value Pr(>|t|)   
(Intercept)                       0.1275167  0.0396214   3.218   0.0015 **
incomeBetween $25,000 and $34,999 0.0002471  0.0725567   0.003   0.9973   
incomeBetween $35,000 and $49,999 0.0396425  0.0607230   0.653   0.5146   
incomeBetween $50,000 and $99,999 0.0610508  0.0530778   1.150   0.2514   
incomeOver $250,000               0.0314597  0.1103214   0.285   0.7758   
incomeUnder $25,000               0.1137905  0.0774979   1.468   0.1435   
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1


Topic 3:

Coefficients:
                                  Estimate Std. Error t value Pr(>|t|)    
(Intercept)                        0.19625    0.03717   5.280 3.25e-07 ***
incomeBetween $25,000 and $34,999  0.01663    0.07021   0.237    0.813    
incomeBetween $35,000 and $49,999  0.08167    0.06335   1.289    0.199    
incomeBetween $50,000 and $99,999  0.00631    0.04795   0.132    0.895    
incomeOver $250,000                0.15149    0.11020   1.375    0.171    
incomeUnder $25,000                0.04267    0.06793   0.628    0.531    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1


Topic 4:

Coefficients:
                                   Estimate Std. Error t value Pr(>|t|)    
(Intercept)                        0.158981   0.038940   4.083 6.35e-05 ***
incomeBetween $25,000 and $34,999  0.005413   0.074508   0.073    0.942    
incomeBetween $35,000 and $49,999 -0.047180   0.060914  -0.775    0.439    
incomeBetween $50,000 and $99,999  0.054605   0.051622   1.058    0.291    
incomeOver $250,000               -0.061632   0.111796  -0.551    0.582    
incomeUnder $25,000               -0.060239   0.063605  -0.947    0.345    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1


Topic 5:

Coefficients:
                                  Estimate Std. Error t value Pr(>|t|)    
(Intercept)                        0.30094    0.04604   6.537 4.77e-10 ***
incomeBetween $25,000 and $34,999 -0.03932    0.08327  -0.472    0.637    
incomeBetween $35,000 and $49,999 -0.06235    0.07328  -0.851    0.396    
incomeBetween $50,000 and $99,999 -0.09631    0.06193  -1.555    0.121    
incomeOver $250,000               -0.07263    0.12620  -0.576    0.566    
incomeUnder $25,000               -0.07602    0.07886  -0.964    0.336    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Code

# estimate effects of ZIP Code
zip_effects <- estimateEffect(formula = 1:k ~ zip,
                               stmobj = stm_change,
                               metadata = CFA_tidy_2)

# get summary
summary(zip_effects)

Call:
estimateEffect(formula = 1:k ~ zip, stmobj = stm_change, metadata = CFA_tidy_2)


Topic 1:

Coefficients:
            Estimate Std. Error t value Pr(>|t|)  
(Intercept)  0.04786    0.23137   0.207   0.8363  
zip59801     0.16868    0.23260   0.725   0.4692  
zip59802     0.10542    0.23225   0.454   0.6504  
zip59803     0.06450    0.23669   0.273   0.7855  
zip59804     0.39097    0.25215   1.551   0.1226  
zip59808     0.14024    0.24130   0.581   0.5618  
zip59821     0.07121    0.36099   0.197   0.8438  
zip59823     0.30359    0.26190   1.159   0.2478  
zip59825     0.13708    0.26029   0.527   0.5990  
zip59826     0.55927    0.40631   1.376   0.1702  
zip59833     0.18341    0.29741   0.617   0.5381  
zip59846     0.07195    0.33489   0.215   0.8301  
zip59847     0.07692    0.25918   0.297   0.7669  
zip59860    -0.02422    0.32294  -0.075   0.9403  
zip59864     0.59609    0.38994   1.529   0.1280  
zip59868     0.11624    0.35782   0.325   0.7456  
zip59908     0.84501    0.33656   2.511   0.0129 *
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1


Topic 2:

Coefficients:
            Estimate Std. Error t value Pr(>|t|)  
(Intercept)   0.8387     0.3343   2.509   0.0129 *
zip59801     -0.6419     0.3338  -1.923   0.0559 .
zip59802     -0.7037     0.3324  -2.117   0.0355 *
zip59803     -0.6122     0.3486  -1.756   0.0806 .
zip59804     -0.6892     0.3480  -1.980   0.0491 *
zip59808     -0.7253     0.3401  -2.132   0.0342 *
zip59821     -0.8260     0.4326  -1.909   0.0577 .
zip59823     -0.5026     0.3591  -1.400   0.1632  
zip59825     -0.5461     0.3721  -1.468   0.1438  
zip59826     -0.5942     0.4894  -1.214   0.2261  
zip59833     -0.7886     0.3849  -2.049   0.0418 *
zip59846     -0.7977     0.4407  -1.810   0.0718 .
zip59847     -0.7608     0.3503  -2.172   0.0310 *
zip59860     -0.8336     0.4258  -1.958   0.0517 .
zip59864     -0.7987     0.4380  -1.824   0.0697 .
zip59868     -0.8309     0.4293  -1.936   0.0544 .
zip59908     -0.8236     0.4277  -1.926   0.0556 .
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1


Topic 3:

Coefficients:
             Estimate Std. Error t value Pr(>|t|)
(Intercept)  0.054642   0.261012   0.209    0.834
zip59801     0.158659   0.264123   0.601    0.549
zip59802     0.192080   0.262144   0.733    0.465
zip59803     0.204604   0.271221   0.754    0.452
zip59804     0.085207   0.273207   0.312    0.755
zip59808     0.228361   0.269399   0.848    0.398
zip59821    -0.038907   0.365350  -0.106    0.915
zip59823     0.116654   0.287839   0.405    0.686
zip59825     0.170334   0.299036   0.570    0.570
zip59826     0.004978   0.370878   0.013    0.989
zip59833     0.412447   0.328869   1.254    0.211
zip59846     0.590087   0.383418   1.539    0.125
zip59847     0.202356   0.290151   0.697    0.486
zip59860     0.029166   0.364310   0.080    0.936
zip59864     0.027134   0.369502   0.073    0.942
zip59868     0.197009   0.424037   0.465    0.643
zip59908    -0.020580   0.366634  -0.056    0.955


Topic 4:

Coefficients:
             Estimate Std. Error t value Pr(>|t|)  
(Intercept)  0.042778   0.260329   0.164   0.8696  
zip59801     0.144437   0.260792   0.554   0.5803  
zip59802     0.096848   0.264349   0.366   0.7145  
zip59803     0.071826   0.269977   0.266   0.7905  
zip59804     0.053355   0.274894   0.194   0.8463  
zip59808     0.056534   0.268183   0.211   0.8333  
zip59821     0.753220   0.399548   1.885   0.0609 .
zip59823     0.009218   0.287808   0.032   0.9745  
zip59825     0.176594   0.293586   0.602   0.5482  
zip59826     0.026547   0.372542   0.071   0.9433  
zip59833     0.085379   0.337101   0.253   0.8003  
zip59846    -0.024108   0.358635  -0.067   0.9465  
zip59847     0.266370   0.298730   0.892   0.3737  
zip59860     0.015152   0.378481   0.040   0.9681  
zip59864     0.069680   0.375566   0.186   0.8530  
zip59868    -0.009627   0.359067  -0.027   0.9786  
zip59908    -0.008059   0.359995  -0.022   0.9822  
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1


Topic 5:

Coefficients:
            Estimate Std. Error t value Pr(>|t|)  
(Intercept) 0.015739   0.290036   0.054   0.9568  
zip59801    0.170499   0.291528   0.585   0.5593  
zip59802    0.310772   0.292193   1.064   0.2888  
zip59803    0.271571   0.301134   0.902   0.3683  
zip59804    0.156567   0.308156   0.508   0.6120  
zip59808    0.301307   0.301814   0.998   0.3193  
zip59821    0.036289   0.411887   0.088   0.9299  
zip59823    0.073325   0.319813   0.229   0.8189  
zip59825    0.060166   0.331664   0.181   0.8562  
zip59826    0.003572   0.408674   0.009   0.9930  
zip59833    0.108267   0.368802   0.294   0.7694  
zip59846    0.147056   0.434504   0.338   0.7354  
zip59847    0.212568   0.336875   0.631   0.5288  
zip59860    0.816065   0.438273   1.862   0.0641 .
zip59864    0.109017   0.433597   0.251   0.8017  
zip59868    0.526910   0.465019   1.133   0.2586  
zip59908    0.008329   0.410320   0.020   0.9838  
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

It appears that each income bracket is slightly positively or negatively associated with each of the five topics but that none of these associations are statistically significant. There are some statistically significant associations between some of the ZIP Codes and particular topics. However, given that I am still exploring how to preprocess my corpus into a DFM and the appropriate number of topics, I am not considering this analysis to be final. Ultimately, the sample is such (too small, not distributed over income brackets and ZIP Codes–some of the ZIP Codes have one respondent!) that it will preclude me from drawing any firm conclusions about associations anyway.

Challenges and Questions Moving Forward

One challenge with topic modeling is that it is up to the researcher to decide how many topics (k) to include in the model. I have no sense of how to do this. Given that the corpus I am working with right now is only a few hundred short documents, I could count them manually but that will not always be feasible. It seems that the best practice is to build models with several different values for k and use various methods to evaluate those models.

In addition to thinking about how to tweak my DFM to make the results of my analysis more revealing, I also need to learn how to evaluate my models to determine which is the most appropriate. Related to that, I need to figure out how to visualize my results.