Generating topics of interests for research communities

Abstract

With ever increasing number of publication venues and research topics, it is becoming difficult for users to find out topics of interest for conferences or research areas. Although we have many popular topic modeling techniques, we still find that conferences are listing their topics of interest using a manual approach. Topics that are generated by existing topic modeling algorithms are good for text categorization, but they are not ideal for displaying to users because they generate topics that are not so readable and are often redundant. In this paper, we propose a novel technique to generate topics of interest using association mining and natural language processing. We show that the topics of interest that are generated by our technique is much more similar to manually written topics of interest compared to existing topic modeling algorithms. Our results show that the proposed method generates meaningful, interpretable topics, and leads to 13.9% higher precision than existing techniques.