Interpreting Topic Results

Running LDA on a collection of text does not return labeled categories. What comes back is a set of topics, and each topic is a ranked list of words. 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. Interpreting the results means turning those ranked word lists into statements a person can check.

The workflow in this article has four moves. 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. The second half of the article applies the same habit of checking to PCA, where the output describes directions rather than word lists.

The words at the top of a topic's list carry the most importance for that topic. Start with the top handful of words and ask what single subject would naturally produce all of them together. A word near the bottom of the list matters less, so it should not drive the naming decision on its own.

Not every topic yields a clean theme. A topic built from songs, band, rock, music, and singing points strongly toward music, and a good first guess depends only on those keywords. Before you check any documents, write down the kind of document you expect the model to assign to the topic most strongly. The guess is a hypothesis to be tested, not a conclusion.

A keyword-based guess is still only a hypothesis. If you want 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 and then read the top of that ranking.

The check can also show that a topic is not what its keywords suggested. If the top documents look unrelated to the words you used to name it, revise the label instead of defending it. Interpretation is a loop between words and documents, and the documents have the final say.

Some topics 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 generic than to force a theme onto it.

The number of topics you request is a setting that changes what you get back. The source compares two runs over the same movie-review collection, one with 10 topics and one with 100. With only 10 topics, each topic must be very broad, because the topics together have to cover every kind of review in the dataset. The topics are easier to read, but each one is general.

Using 100 topics makes the analysis much harder to interpret. It also makes it more likely that topics can specialize on interesting subsets of the data, such as a particular genre or a particular kind of rating. So granularity is a trade-off. Fewer topics give broad topics that are easier to read. More topics give specialized topics that are harder to interpret.

You can also ask how much weight each topic gets overall. Sum each topic's weights across all the reviews to get a total for every topic. The source 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 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 source concludes that LDA mostly discovered two 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 is producing something a reader can use.

After a PCA object has been fitted, the directions it found are kept in an attribute called components_. You do not need to compute them again. You read them from the fitted object. Each direction is one principal component, and the components are ordered by importance. The first one captures the largest share of the variance in the data, the second captures the next largest, and so on.

The shape tells you the layout before you look at a single value. The first number is the count of components, here 2. The second number is the count of original features, here 30. So the array has one row per component and one column per feature. Each row is a direction, and each column is a feature that the direction draws on.

Reading the matrix correctly depends on remembering what rows and columns stand for. A row answers the question which combination of features makes up this component. A column answers the question how much one feature contributes to each component. Reading in the wrong direction produces confident but meaningless statements, so check the orientation before anything else.

A principal component is never built from a single measurement. Its direction is spread across all of the original features at once, so every feature has some weight in it. Write the original features as $x_1$ through $x_{30}$. Each weight $w$ is one entry of components_, and the first row holds the 30 weights for the first component. Each weight says how strongly that feature pulls the component's direction.

This is why the axes are hard to name. An axis called after one measurement would be easy to explain. An axis built from thirty interacting measurements is not, because its meaning is a pattern across all of them. The weights can also disagree in sign. Some features push the component in one direction while others push it the other way. A mixed-sign pattern tells you the component is contrasting groups of features rather than measuring one overall quantity.

Two features that carry the same sign in a component tend to move together in the data. If one feature is high, the others sharing its sign are likely to be high too. Features with opposite signs trade off against each other along that axis. A table of 60 numbers is hard to scan, so a heat map turns each coefficient into a colored cell, letting you see the sign pattern and relative size at a glance.

The source demonstrates this with the first two components of a breast cancer dataset. The columns are the 30 features and the two rows are the components. In the heat map, the first component has a single sign across its row, so its cells are uniform in tone. This marks a general correlation among the features. The second component's row is split between two tones, reflecting its mixed signs. Both rows touch every column, which is the visual version of the point that every feature participates.

A heat map makes the sign pattern and relative weight sizes visible, but interpretations should always be checked against the printed numbers. The picture is a convenient summary, not a replacement for the values in components_.

When an interpretation disagrees with what you expected, the useful question is where in the chain the mismatch enters. The chain runs from the fitted object, to the components_ attribute, to the printed numbers, to the written description. Most errors happen at one of two links: the orientation of the array, or the text you wrote about the numbers.