A clustering-based approach on sentiment analysis

This paper introduces the clustering-based sentiment analysis approach which is a new approach to sentiment analysis that has competitive advantages over the two existing kinds of approaches: symbolic techniques and supervised learning methods.

Abstract

This paper introduces the clustering-based sentiment analysis approach which is a new approach to sentiment analysis. By applying a TF-IDF weighting method, voting mechanism and importing term scores, an acceptable and stable clustering result can be obtained. It has competitive advantages over the two existing kinds of approaches: symbolic techniques and supervised learning methods. It is a well performed, efficient, and non-human participating approach on solving sentiment analysis problems.