Sentiment analysis based on clustering: a framework in improving accuracy and recognizing neutral opinions

Authors:
Gang Li;Fei Liu
Affiliations:
Department of Computer Science and Computer Engineering, La Trobe University, Melbourne, Australia;Department of Computer Science and Computer Engineering, La Trobe University, Melbourne, Australia
Venue:
Applied Intelligence
Year:
2014

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Abstract

Clustering-based sentiment analysis is a novel approach for analyzing opinions expressed in reviews, comments or blogs. In contrast to the two traditional mainstream approaches (supervised learning and symbolic techniques), the clustering-based approach is able to produce basically accurate analysis results without any human participation, linguist knowledge or training time.This paper introduces new techniques designed to extend the capability of the clustering-based sentiment analysis approach in two aspects: firstly by applying opposite opinion contents processing and non-opinion contents processing techniques to further enhance accuracy; and secondly by using a modified voting mechanism and distance measurement method to conduct fine-grained (three classes) sentiment analysis. According to the experiment results, the clustering-based approach is proven to be useful in performing high quality sentiment analysis result, and suitable for recognizing neutral opinions.