Machine Learning for Algorithmic Trading - Second Edition

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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 ...