Improving interpretations of topic modeling in microblogs

Abstract

Topic models were proposed to detect the underlying semantic structure of large collections of text documents to facilitate the process of browsing and accessing documents with similar ideas and topics. Applying topic models to short text documents to extract meaningful topics is challenging. The problem becomes even more complicated when dealing with short and noisy micro-posts in Twitter that are about one general topic. In such a case, the goal of applying topic models is to extract subtopics. This results in topics represented by similar sets of keywords, which in turn makes the process of topic interpretation more confusing. In this paper we propose a new method that incorporates Twitter-LDA, WordNet, and hashtags to enhance the keyword labels that represent each topic. We emphasize the importance of different keywords to different topics based on the semantic relationships and the co-occurrences of keywords in hashtags. We also propose a method to find the best number of topics to represent the text document collection. Experiments on two real-life Twitter datasets on fashion suggest that our method performs better than the original Twitter-LDA in terms of perplexity, topic coherence, and the quality of keywords for topic labeling.