Choosing the Number of Topics

Suppose you run LDA on a collection of movie reviews and it returns a set of topics. You might expect the output to say something like horror films or award shows. It does not. Each topic comes back as a ranked list of words, and nothing more. The model has no idea that a topic is about horror films or award shows. It only knows which words tend to appear together across the collection. Your job is to turn those ranked word lists into statements a person can check.

This lesson follows one practical workflow. You look at the highest-ranking words of a topic, guess what the topic is about, verify that guess against the documents the model assigned to it, and only then decide how much weight the topic deserves. Along the way you will see how the total number of topics changes what you get back.

The words at the top of a topic's list carry the most importance for that topic. Their combined meaning is the best first clue to the topic's theme. Words further down the list still belong to the topic, but they usually matter less for naming it. So when you read a topic, start with the top handful of words and ask what single subject would naturally produce all of them together.

A keyword-based guess is still only a hypothesis. To make further inferences from a discovered topic, confirm your intuition by looking at the documents assigned to that topic. The model records a weight for every topic in every document. You can sort the documents by how strongly a given topic appears in each one, then read the top of that ranking.

In the movie-review example, topic 45 was checked this way. The reviews were sorted by that topic's weight and the first few were read. All of them were about music in some way. One praised a film because of its music. One described a comeback tour by a fictional rock band. Another covered a musical directed by a classic Hollywood choreographer. The keyword guess held up, and the documents also showed the range of what the topic covers.

The number of topics you ask for is a setting that changes what you get back. The movie-review example compares two runs over the same collection: one with 10 topics and one with 100. This setting controls granularity. Fewer topics give broad topics that are easier to read. More topics give specialized topics that are harder to interpret.

With only 10 topics, each topic has to be very broad, because the topics together must cover every kind of review in the dataset. With 100 topics, the analysis becomes much harder to interpret, but topics are more likely to specialize on interesting subsets of the data. Breadth and specificity pull in opposite directions, so the choice of K is a trade-off between how easy a topic is to read and how narrow its subject can be.

A practical way to use this is to try several candidate values of K, read the top words and top documents of each resulting topic, and compare how well each run's topics hold together. Stop when the topics are specific enough to be useful and still readable. The routine is repeatable, but the final check on each topic is always the documents.

Not every topic yields a clean theme. Some are built mostly from very common words such as didn, saw, am, thought, years, and book. These words appear in nearly every kind of review, so a topic made of them does not describe a subject. It captures general phrasing. In that case it is more honest to label the topic as generic than to force a theme onto it.

Beyond single topics, you can ask how much weight each topic receives overall. The method is to sum each topic's weights across all the reviews, which gives one total per topic. The movie-review example names each topic by its two most common words and plots these totals as horizontal bars, split into two columns of fifty topics each, so the whole model can be compared at a glance.

The chart shows that the most important topics are not all meaningful. The top topic is mostly stopwords with a slight negative direction. Topic 16 is clearly about bad reviews. Several genre-specific topics follow, and topics 36 and 37 both contain laudatory words. Read this way, the chart tells you which topics dominate the collection and prompts you to check whether a dominant topic is a real theme or a filler artifact.

Taken together, the movie-review example found two main kinds of topics: genre-specific topics and rating-specific topics, along with several less specific ones. This matters because most reviews mix a few comments about the movie itself with comments that justify or emphasize the rating. A model that separates these two layers produces something a reader can use.