While it is technically trivial to search for the company name to predict the company a new article refers to,
it often leads to incorrect results. In this article, we compare the two approaches bag-of-words
with k-nearest neighbors and Latent Dirichlet Allocation with k-nearest neighbor by
assessing their applicability for predicting the S\&P 500 company which is mentioned in a
business news article or press release. Both approaches are evaluated on a corpus of 13k documents
containing 84\% news articles and 16\% press releases. While the bag-of-words
approach yields accurate predictions, it is highly inefficient due to its gigantic feature space.
The Latent Dirichlet Allocation approach, on the other hand, manages to achieve roughly the same
prediction accuracy (0.58 instead of 0.62) but reduces the feature space by a factor of seven