So far we looked at analyzing text to understand better what the text or corpus consists of. When we tried to POS-tag or NER-tag, we were interested in knowing what kind of words were presented in our documents, and when we topic-modeled, we wanted to know the underlying topics which could be hidden in our texts. Sure, we could use our topic models to attempt to cluster articles, but that isn't its purpose; we would be silly to expect great results if we tried this, too. Remember that since the purpose of topic modeling is to find hidden themes in a corpus and not to group documents together, our methods are not optimized for the task. For example, after we perform topic modeling, a document can be made of 30% topic 1, 30% topic 2, and 40% topic 3. In such a case, we cannot use this information to cluster.
Let us now start exploring how to use machine learning...
