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Fine-grained sentiment analysis for measuring customer satisfaction using an extended set of fuzzy linguistic hedges
Authors
Muhammad Zubair Asghar
Ibrahim A. Hameed
+5 more
Nosheen Jillani
Asad Khattak
Waqas Tariq Paracha
Furqan Khan Saddozai
Umair Younis
Publication date
1 January 2020
Publisher
ZU Scholars
Abstract
© 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
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ZU Scholars (Zayed University)
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oai:zuscholars.zu.ac.ae:works-...
Last time updated on 03/12/2021
NORA - Norwegian Open Research Archives
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Last time updated on 19/11/2020