Topics

This chapter focuses on topic modelling, i.e. the automatic extraction of topics or themes from a corpus. Topic modelling goes a step further than keywords in the automatic identification of the contents of a corpus. Two types of approaches are considered, discussed, and contrasted. On the one hand, those that I dub “traditional”, as illustrated by the LDA and NMF algorithms, and, on the other, embeddings-based approaches, which largely surpass the former in the quality of results. The weakest aspect of topic modelling tools in general is the lack actual labels for the extracted topics, since all they return is a set of loosely related keywords that collectively identify the topic. In the last experiment I describe an approach that uses the power of Large Language Models to effectively derive high-quality labels for the extracted topics.