Fine-Grained Sentiment Analysis for Measuring Customer Satisfaction Using an Extended Set of Fuzzy Linguistic Hedges
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...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Springer
2020-06-01
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| Series: | International Journal of Computational Intelligence Systems |
| Subjects: | |
| Online Access: | https://www.atlantis-press.com/article/125941251/view |
| _version_ | 1818228785552556032 |
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| author | Asad Khattak Waqas Tariq Paracha Muhammad Zubair Asghar Nosheen Jillani Umair Younis Furqan Khan Saddozai Ibrahim A. Hameed |
| author_facet | Asad Khattak Waqas Tariq Paracha Muhammad Zubair Asghar Nosheen Jillani Umair Younis Furqan Khan Saddozai Ibrahim A. Hameed |
| author_sort | Asad Khattak |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-12T10:08:14Z |
| format | Article |
| id | doaj.art-35288814ae6d4390885a06673da54d06 |
| institution | Directory Open Access Journal |
| issn | 1875-6883 |
| language | English |
| last_indexed | 2024-12-12T10:08:14Z |
| publishDate | 2020-06-01 |
| publisher | Springer |
| record_format | Article |
| series | International Journal of Computational Intelligence Systems |
| spelling | doaj.art-35288814ae6d4390885a06673da54d062022-12-22T00:27:52ZengSpringerInternational Journal of Computational Intelligence Systems1875-68832020-06-0113110.2991/ijcis.d.200513.001Fine-Grained Sentiment Analysis for Measuring Customer Satisfaction Using an Extended Set of Fuzzy Linguistic HedgesAsad KhattakWaqas Tariq ParachaMuhammad Zubair AsgharNosheen JillaniUmair YounisFurqan Khan SaddozaiIbrahim A. HameedIn 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.https://www.atlantis-press.com/article/125941251/viewCustomer satisfactionFine-grained sentiment analysisFuzzy logicLinguistic hedgesMembership function |
| spellingShingle | Asad Khattak Waqas Tariq Paracha Muhammad Zubair Asghar Nosheen Jillani Umair Younis Furqan Khan Saddozai Ibrahim A. Hameed Fine-Grained Sentiment Analysis for Measuring Customer Satisfaction Using an Extended Set of Fuzzy Linguistic Hedges International Journal of Computational Intelligence Systems Customer satisfaction Fine-grained sentiment analysis Fuzzy logic Linguistic hedges Membership function |
| title | Fine-Grained Sentiment Analysis for Measuring Customer Satisfaction Using an Extended Set of Fuzzy Linguistic Hedges |
| title_full | Fine-Grained Sentiment Analysis for Measuring Customer Satisfaction Using an Extended Set of Fuzzy Linguistic Hedges |
| title_fullStr | Fine-Grained Sentiment Analysis for Measuring Customer Satisfaction Using an Extended Set of Fuzzy Linguistic Hedges |
| title_full_unstemmed | Fine-Grained Sentiment Analysis for Measuring Customer Satisfaction Using an Extended Set of Fuzzy Linguistic Hedges |
| title_short | Fine-Grained Sentiment Analysis for Measuring Customer Satisfaction Using an Extended Set of Fuzzy Linguistic Hedges |
| title_sort | fine grained sentiment analysis for measuring customer satisfaction using an extended set of fuzzy linguistic hedges |
| topic | Customer satisfaction Fine-grained sentiment analysis Fuzzy logic Linguistic hedges Membership function |
| url | https://www.atlantis-press.com/article/125941251/view |
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