Using Topic and Subjectivity Analysis for Overlapped Co-clustering Documents

摘要

The purpose of text mining is to extract meaningful information from documents for various applications. One such application is document clustering. Document clustering refers to the clustering of similar documents into a segment. However, the numerous sentiments, emotions, and concepts involved in documents complicate the task of document clustering. In this paper, we combine the results of topic analysis and sentiment analysis to perform the co-clustering of documents. In contrast to previous papers, the key characteristic of the proposed method is soft clustering, and it considers topics and subjectivity (including emotions and sentiments) simultaneously. The empirical results indicate that the proposed method can effectively cluster documents based on the MANOVA test. In addition, the proposed method also provides a flexible way to co-cluster documents based on topics and/or subjectivity.