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Structural Topic Modeling

Wouter van Atteveldt & Kasper Welbers Last updated: 2019-04

Structural Topic Models

Structural topic models are an extension of LDA that allow us to explicitly model text metadata such as date or author as covariates of the topic prevalence and/or topic words distributions. In the stm package it has excellent support for R (see http://structuraltopicmodel.com) (although fitting the models can be a bit slower than fitting regular LDA models)

In this document we will model the inaugural addresses of recent presidents. First, we read the data into a (quanteda) corpus to make sure the metadata is kept with the text, and then we create a dfm:

library(quanteda)
corpus = data_corpus_inaugural %>% corpus_subset(Year > 1980) %>% 
  corpus_reshape(to = "paragraphs")
dfm = dfm(corpus, remove_punct=T, remove=stopwords("english")) %>% dfm_trim(min_docfreq = 2)
head(docvars(dfm))

Now, we can use the stm function to fit a topic model. First, let's fit one without any covariates:

library(stm)
m = stm(dfm, K = 10, max.em.its = 10)

(Note: max.em.hits sets the number of iterations if the model doesn't converge earlier. 10 is probably too low, but sinec this is for demonstration purposes it helps that it finishes more quickly)

This words similarly to a regular topic model, but it also models correlations between topics. To inspect topic model results, we can use functions from the stm package:

plot(m, type="summary", labeltype = "frex")
labelTopics(m, topic=8)

By default, stm gives multiple ways of inspecting top words: simple highest probabibility (the first row) and three ways of finding the most 'typical' words by focusing on words that are less common in other topics.

We can also plot the words per topic and the words 'between' two topics:

cloud(m, topic=8)
plot(m, type="perspectives", topics=c(8,9))

Prevalence covariate

Now, let's model year as a covariance of the prevalence:

m = stm(dfm, K = 10, prevalence =~ Year, max.em.its = 10)

Besides the functions above, we can now also model the effect of year using the estimateEffect function:

prep <- estimateEffect(1:10 ~ Year, stmobj = m, meta = docvars(dfm))
summary(prep, topics=1:8)

And plot the topic prevalences over time:

plot(prep, "Year", method = "continuous", topics = c(8,9), model = m)

Spline function

A continuous covariate (like year above) is modeled as a linear relation. To model a non-linear relation (i.e. to allow a topic to peak and decline), it can be useful to use a spline function, which decomposes the continuous function into a mixture of separate variables each modeling a specific (non-discrete) period.

To see how splines work, let's have a look at a 4-spline model of the years in our data

library(tidyverse)
splines = cbind(data.frame(year=1980:2000), s(1980:2000, 3))
splines = reshape2::melt(splines, id.var="year")
ggplot(splines, aes(x=year, y=value, color=variable, group=variable)) + geom_line()

So as you can see, this creates 3 "soft dummies", each representing a unique period. To use this for modeling, simply insert s(Year, 3) where we used Year above:

m = stm(dfm, K = 10, prevalence =~ s(Year, 3), max.em.its = 10)
prep <- estimateEffect(1:10 ~ s(Year, 3), stmobj = m, meta = docvars(dfm))
plot(prep, "Year", method = "continuous", topics = c(8,9), model = m)

Content covariates

Finally, let's also add President as a content covariate, meaning that each president can use different words to discuss the same topic. We add this covariate using the content=~ argument:

m = stm(dfm, K = 10, content =~ President, max.em.its = 10)

If we now ask for the top terms, we get the terms per President as well as per topic:

labelTopics(m, topics = 8)

We can also plot the word use per president as a 'perspective plot':

plot(m, type="perspectives", topics=8, covarlevels = c("Bush", "Obama"))

Finally, to find texts where a specific president talks about a specific topic, you can use:

findThoughts(m, texts = texts(corpus), topics = 8, where=President=="Obama", meta=docvars(dfm))

Correlation structure

Finally, we can plot the correlation structure of the model:

corr = topicCorr(m)
plot(corr)

More complex models

You're not limited to a single covariate. For example, the following would use both year and president for prevalence, and president as content covariate:

m = stm(dfm, K = 10, content =~ President, prevalence =~ Year + President, max.em.its = 10)

And more!

STM contains many more useful functions, for example for selecting the best model or K, and determining coherence. See the vignette (from http://www.structuraltopicmodel.com/) and the help files for the stm package!

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