A topic modeling tool takes a single text (or corpus) and looks for patterns in the use of words; it is an attempt to inject semantic meaning into vocabulary. Before you begin with topic modeling, you should ask yourself whether or not it is likely to be useful for your project. Matthew Kirschenbaum’s Distant Reading and Stephen Ramsay’s Reading Machines are good places for beginning to understand in which circumstances a technique such as this could be most effective. As with all tools, just because you can use it, doesn’t necessarily mean that you should. If you are working with a small number of documents (or even a single document) it may well be that simple frequency counts are sufficient, in which case something like Voyant Tools might be appropriate. However, if you have hundreds of documents from an archive and you wish to understand something of what the archive contains without necessarily reading every document, then topic modeling might be a good approach.

Topic models represent a family of computer programs that extract topics from texts. A topic to the computer is a list of words that occur in statistically meaningful ways. A text can be an email, a blog post, a book chapter, a journal article, a diary entry – that is, any kind of unstructured text. By unstructured we mean that there are no computer-readable annotations that tell the computer the semantic meaning of the words in the text.

Topic modeling programs do not know anything about the meaning of the words in a text. Instead, they assume that any piece of text is composed (by an author) by selecting words from possible baskets of words where each basket corresponds to a topic. If that is true, then it becomes possible to mathematically decompose a text into the probable baskets from whence the words first came. The tool goes through this process over and over again until it settles on the most likely distribution of words into baskets, which we call topics.

There are many different topic modeling programs available; this tutorial uses one called MALLET. If one used it on a series of political speeches for example, the program would return a list of topics and the keywords composing those topics. Each of these lists is a topic according to the algorithm. Using the example of political speeches, the list might look like:

1. Job Jobs Loss Unemployment Growth
2. Economy Sector Economics Stock Banks
3. Afghanistan War Troops Middle-East Taliban Terror
4. Election Opponent Upcoming President
5. … etc.

By examining the keywords we can discern that the politician who gave the speeches was concerned with the economy, jobs, the Middle East, the upcoming election, and so on.

As Scott Weingart warns, there are many dangers that face those who use topic modeling without fully understanding it. For instance, we might be interested in word use as a proxy for placement along a political spectrum. Topic modeling could certainly help with that, but we have to remember that the proxy is not in itself the thing we seek to understand – as Andrew Gelman demonstrates in his mock study of zombies using Google Trends. Ted Underwood and Lisa Rhody (see Further Reading) argue that we as historians would be better to think of these categories as discourses; however for our purposes here we will continue to use the word: topic.