Fuzzy information granulation towards interpretable sentiment analysis

Abstract

Sentiment analysis, which is also referred to as opinion mining, is aimed at recognising the attitude or emotion of people through natural language processing, text analysis, and computational linguistics. In the past years, many studies have been focused on sentiment classification in the context of machine learning, e.g., to identify that an instance of sentiments is positive or negative. In particular, the bag-of-words method has been popularly used for transforming textual data into structural data, to enable machine learning algorithms to be used directly for tasks of sentiment classification. Through the use of the bag-of-words method, each single word in a set of textual instances is turned into a single attribute in a structural data set transformed from the textual data set. This form of transformation usually results in massively high dimensionality and thus impacts negatively on the interpretation of sentiment analysis models. In this paper, we propose an approach based on fuzzy information granulation towards interpretable sentiment analysis models. We review the concepts and techniques of granular computing in general, and focus on the characteristics of fuzzy information granulation in particular. Based on this review and on previous experimental results on movie data, we position the research of sentiment analysis in the context of fuzzy information granulation.