Topic Modeling – Summarizing Financial News
In the last chapter, we used the bag-of-words (BOW) model to convert unstructured text data into a numerical format. This model abstracts from word order and represents documents as word vectors, where each entry represents the relevance of a token to the document. The resulting document-term matrix (DTM)—or transposed as the term-document matrix—is useful for comparing documents to each other or a query vector for similarity based on their token content and, therefore, finding the proverbial needle in a haystack. It provides informative features to classify documents, such as in our sentiment analysis examples.
However, this document model produces both high-dimensional data and very sparse data, yet it does little to summarize the content or get closer to understanding what it is about. In this chapter, we will use unsupervised machine learning to extract hidden themes from documents using topic modeling....
