If you’ve ever wondered how to analyze large sets of textual data and uncover hidden topics within, you’re in for a treat! Structural Topic Modeling (STM) is your go-to method, merging machine learning techniques with content analysis. This guide will walk you through the process of implementing STM using the R programming language, and introduce you to a structured workflow that can enhance your research projects.

Table of Contents

• What is a Structural Topic Model?
• Materials
• Dataset
• An STM Workflow Example
  • A. Ingest
  • B. Prepare
  • C. Estimate
  • D. Evaluate
  • E. Understand
  • F. Visualise
• References
• Want to Contribute?

What is a Structural Topic Model?

A Structural Topic Model is like a treasure map for your textual data. It helps researchers uncover topics and explore how these topics relate to various document metadata. By leveraging document-level information, STM enhances our understanding of textual content, making it a vital tool for hypothesis testing. You can find more detailed definitions and technical insights in the stm R package documentation.

Materials

To embark on your STM journey, you will need the following:

• stm R Package – The backbone for structural topic modeling.
• STM Vignette – Provides a technical overview and hands-on examples.
• D-Lab Text Analysis Working Group – A valuable source of scripts for learning.

Dataset

The dataset you’ll use for this STM exercise is the Carnegie Mellon University 2008 Political Blog Corpus, which comprises blog posts discussing American politics from 2008. This corpus is also included in the repository for easier access.

An STM Workflow Example

Now that we have our materials and dataset, let’s walk through a structured workflow to implement STM in R. Think of it like cooking a multi-course meal—there are specific steps to follow, and you can modify the recipe to suit your taste!

A. Ingest

Start by loading the necessary R libraries. This is akin to gathering all your ingredients before you start cooking. Here’s how to do it:

library(stm)
library(igraph)
library(stmCorrViz)

Load your data, which includes a CSV file and a pre-processed RData file to save time:

data <- read.csv('poliblogs2008.csv')
load('VignetteObjects.RData')

B. Prepare

For preparation, we need to clean and structure our data. This is like chopping vegetables before cooking. Use the following functions:

processed <- textProcessor(data$documents, metadata=data)
out <- prepDocuments(processed$documents, processed$vocab, processed$meta)

Check how many words and documents might be removed using different thresholds:

plotRemoved(processed$documents, lower.thresh=seq(1,200, by=100))

C. Estimate

Now, we’ll estimate our model, determining how topics appear across different documents. Think of this as the cooking phase, where you heat and combine your ingredients:

poliblogPrevFit <- stm(out$documents, out$vocab, K=20, prevalence=~rating+s(day), 
max.em.its=75, data=out$meta, init.type=Spectral, seed=8458159)

D. Evaluate

In this phase, we check our model's quality; it’s similar to tasting your dish to ensure it is seasoned correctly. Use the following function to select the best model:

poliblogSelect <- selectModel(out$documents, out$vocab, K=20, prevalence=~rating+s(day), 
max.em.its=75, data=meta, runs=20, seed=8458159)

E. Understand

Now that your dish is prepared, you need to understand its flavors. This involves interpreting model results:

labelTopicsSel <- labelTopics(poliblogPrevFit, c(3,7,20))

F. Visualise

Finally, it’s time to present your creation—visualizing your topics and their relationships to metadata:

plot(poliblogPrevFit, type=perspectives, topics=7)

References

• Eisenstein, J., Xing E. (2010) The CMU 2008 Political Blog Corpus.
• Roberts, M.E., et al. (2016) A model of text for experimentation in the social sciences.
• Roberts, M.E., et al. (2017) stm: Estimation of the Structural Topic Model.

Troubleshooting

As you navigate the process of implementing STM, you may encounter challenges. Here are some troubleshooting tips:

• If your R libraries fail to load, check if they are installed correctly. You can do this by running install.packages("package_name").
• If your model doesn’t converge, ensure your dataset is cleaned properly and that parameters are set correctly.
• For visualizations not appearing, make sure the relevant libraries (like ggplot2 for plotting) are installed and loaded.

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