The main goal of topic modeling in NLP is to analyze a corpus, to identify common topics among documents. In this context, even if we talk about semantics, this concept has a particular meaning, driven by a very important assumption. A topic derives from the usage of particular terms in the same document, and it is confirmed by the multiplicity of different documents where the first condition is true.
In other words, we don't consider human-oriented semantics but a statistical modeling that works with meaningful documents (this guarantees that the usage of terms is aimed to express a particular concept, and, therefore, there's a human semantic purpose behind them). For this reason, the starting point of all our methods is an occurrence matrix, normally defined as a document-term matrix (we have already discussed count vectorizing and TF-IDF in Chapter...
