Be positive
Ryan M. Omizo · Studies in corpus linguistics · 2023
Abstract This chapter proposes a novel method that deploys non-negative matrix factorization to extract topic models from texts. This topic modeling process reveals how terms and DocuScope Language Action Type Analysis (LATs) align, providing robust information on what texts are about and how they are organized rhetorically. Moreover, the non-negative nature of the topics means that each derived topic can be viewed as a sum of topical features, which can greatly ease the interpretive process. To elucidate and benchmark this method, I apply it to a well-known 20 Newsgroups dataset and sample the results.