Abstract
In recent years, the boom in social media sites such as Facebook and Twitter has brought people together for the sharing of opinions, sentiments, emotions, and experiences about products, events, politics, and other topics.In particular, sentiment-based applications are growing in popularity among individuals and businesses for the making of purchase decisions. Fuzzy-based sentiment analysis aims at classifying customer sentiment at a fine-grained level. This study deals with the development of a fuzzy-based sentiment analysis by extending fuzzy hedges and rule-sets for a more efficient classification of customer sentiment and satisfaction. Prior studies have used a limited number of linguistic hedges and polarity classes in their rule-sets, resulting in the degraded efficiency of their fuzzy-based sentiment analysis systems. The proposed analysis of the current study classifies customer reviews using fuzzy linguistic hedges and an extended rule-set with seven sentiment analysis classes, namely extremely positive, very positive, positive, neutral, negative, very negative, and extremely negative. Then, a fuzzy logic system is applied to measure customer satisfaction at a fine-grained level. The experimental results demonstrate that the proposed analysis has an improved performance over the baseline works.
Highlights
Sentiment analysis (SA) is a challenging research area that uses natural language processing (NLP), text mining, and computational linguistics
Answer to RQ1: What Is the Role of an Extended Set of Fuzzy Linguistic Hedges with Respect to the Efficient Sentiment Classification of User Reviews for Measuring Customer Satisfaction?
To answer RQ1, we extended a set of fuzzy linguistic hedges
Summary
Sentiment analysis (SA) is a challenging research area that uses natural language processing (NLP), text mining, and computational linguistics. It has a wide range of applications in different domains, such as in politics, education, business, and others fields [1]. Businesses use sentiment-based applications to analyze user feedback, brand loyalty, and customer satisfaction with their products, assisting in quality improvement to increase revenue [2,3]. Most of the most recent work on SA for customer feedback analysis has been based on the binary classification of customer reviews (i.e., positive or negative) with less focus on a fine-grained sentiment classification. Fuzzy-based SA aims at classifying sentiments using multiple
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