Sentiment Classification Of Chinese Reviews Research Articles

A multi-granularity fuzzy computing model for sentiment classification of Chinese reviews

With the rapid growth of user-generated contents online, unsupervised methods which do not need to use labeled training data have become increasingly important in sentiment classification. But the performance of unsupervised methods is unsatisfactory. This is because sentence structure and ambiguit y of sentiment intensity are usually ignored in existing unsupervised methods. To address these problems, we propose a multi-granularity fuzzy computing model which involves two innovations. Firstly, we come up with a multi-granularity computing method to compute sentiment intensity of reviews. To be specific, we deconstruct those reviews into three levels of language units—words, phrases and sentences, and consequently manage to compute the sentiment intensity of reviews by combining rule-based methods and statistic-based methods. Secondly, a fuzzy classifier is constructed to solve the ambiguity of sentiment intensity. Furthermore, two different self-supervised methods using pseudo-labeled training data are proposed to learn the optimum parameters of the fuzzy classifier. Experimental results in four different datasets prove that our model improves 6.25% more accuracy on average than the competitive baselines in sentiment classification of Chinese reviews.

An Empirical Study of Unsupervised Sentiment Classification of Chinese Reviews

This paper is an empirical study of unsupervised sentiment classification of Chinese reviews. The focus is on exploring the ways to improve the performance of the unsupervised sentiment classification based on limited existing sentiment resources in Chinese. On the one hand, all available Chinese sentiment lexicons — individual and combined — are evaluated under our proposed framework. On the other hand, the domain dependent sentiment noise words are identified and removed using unlabeled data, to improve the classification performance. To the best of our knowledge, this is the first such attempt. Experiments have been conducted on three open datasets in two domains, and the results show that the proposed algorithm for sentiment noise words removal can improve the classification performance significantly.