Topic-Modeling Algorithms
Topic-modeling algorithms operate on the following assumptions:
- Topics contain a set of words.
- Documents are made up of a set of topics.
Topics can be considered to be a weighted collection of words. After these common assumptions, different algorithms diverge in how they go about discovering topics. In the upcoming sections, we will cover in detail three topic-modeling algorithms—namely LSA, LDA, and HDP. Here, the term latent (the L in these acronyms) refers to the fact that the probability distribution of the topics is not directly observable. We can observe the documents and the words but not the topics.
Note
The LDA algorithm builds on the LSA algorithm. In this case, similar acronyms are indicative of this association.
Latent Semantic Analysis (LSA)
We will start by looking at LSA. LSA actually predates the World Wide Web. It was first described in 1988. LSA is also known by an alternative name, Latent Semantic Indexing...
