Source of Publication
International Journal of Computational Intelligence Systems
© 2020 The Authors. Published by Atlantis Press SARL. 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.
Customer satisfaction, Fine-grained sentiment analysis, Fuzzy logic, Linguistic hedges, Membership function
Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial 4.0 License
Khattak, Asad; Paracha, Waqas Tariq; Asghar, Muhammad Zubair; Jillani, Nosheen; Younis, Umair; Saddozai, Furqan Khan; and Hameed, Ibrahim A., "Fine-grained sentiment analysis for measuring customer satisfaction using an extended set of fuzzy linguistic hedges" (2020). All Works. 1682.
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Open Access Type
Gold: This publication is openly available in an open access journal/series