Fine-Grained Sentiment Analysis for Measuring Customer Satisfaction Using an Extended Set of Fuzzy Linguistic Hedges

Authors

Asad Khattak¹, Waqas Tariq Paracha², Muhammad Zubair Asghar^2, , Nosheen Jillani², Umair Younis², Furqan Khan Saddozai^2, , Ibrahim A. Hameed^{3, *,}

¹College of Technological Innovation, Zayed University, 144534, Abu Dhabi Campus, UAE

²Institute of Computing and Information Technology, Gomal University, DIKhan (KP), Pakistan

³Department of ICT and Natural Sciences, Faculty of Information Technology and Electrical Engineering, Hovedbygget, B316, Ålesund, Norway

^*Corresponding author. Email: ibib.hameed@gmail.com

DOI

10.2991/ijcis.d.200513.001How to use a DOI?

Keywords

Customer satisfaction; Fine-grained sentiment analysis; Fuzzy logic; Linguistic hedges; Membership function

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.

Copyright

© 2020 The Authors. Published by Atlantis Press SARL.

Open Access

This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - JOUR
AU  - Asad Khattak
AU  - Waqas Tariq Paracha
AU  - Muhammad Zubair Asghar
AU  - Nosheen Jillani
AU  - Umair Younis
AU  - Furqan Khan Saddozai
AU  - Ibrahim A. Hameed
PY  - 2020
DA  - 2020/06/11
TI  - Fine-Grained Sentiment Analysis for Measuring Customer Satisfaction Using an Extended Set of Fuzzy Linguistic Hedges
JO  - International Journal of Computational Intelligence Systems
SP  - 744
EP  - 756
VL  - 13
IS  - 1
SN  - 1875-6883
UR  - https://doi.org/10.2991/ijcis.d.200513.001
DO  - 10.2991/ijcis.d.200513.001
ID  - Khattak2020
ER  -