Periodic insight: Multilingual reputation generation system through daily opinion mining analysis
The global trend of individuals expressing their opinions on Twitter has led to a substantial number of user-generated reviews across various brands, products, and services. As a result, there is a growing need for automated systems capable of analyzing and interpreting this extensive content. In re...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-06-01
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| Series: | Results in Engineering |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2590123025006966 |
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| author | Achraf Boumhidi Abdessamad Benlahbib Erik Cambria El Habib Nfaoui |
| author_facet | Achraf Boumhidi Abdessamad Benlahbib Erik Cambria El Habib Nfaoui |
| author_sort | Achraf Boumhidi |
| collection | DOAJ |
| description | The global trend of individuals expressing their opinions on Twitter has led to a substantial number of user-generated reviews across various brands, products, and services. As a result, there is a growing need for automated systems capable of analyzing and interpreting this extensive content. In response, reputation generation systems have been developed to extract valuable insights from both textual and numerical reviews. However, many of these systems have significant limitations. Firstly, most of them are limited to processing English text, which poses a barrier for analyzing reviews in other languages. Also, they are incapable of handling immediate data influx, they often fall short in providing up-to-date and accurate reputation assessments. Therefore, we propose a two-phase system for generating accurate reputation values. In the first phase, data preparation, the system incorporates review translation to English, spam filtering, and sarcasm detection to address limitations of language processing and enhance data quality. This prepares the data for the second phase, reputation generation, which utilizes state-of-the-art, aspect-based sentiment analysis techniques, offering a novel approach to calculating reputation by considering specific aspects of products or services. Experimental results conducted on multiple datasets show the efficacy of the proposed system compared with previous reputation generation systems. |
| format | Article |
| id | doaj-art-69e04fd0980842cb88d9747e5db33f55 |
| institution | DOAJ |
| issn | 2590-1230 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Results in Engineering |
| spelling | doaj-art-69e04fd0980842cb88d9747e5db33f552025-08-20T02:40:40ZengElsevierResults in Engineering2590-12302025-06-012610461910.1016/j.rineng.2025.104619Periodic insight: Multilingual reputation generation system through daily opinion mining analysisAchraf Boumhidi0Abdessamad Benlahbib1Erik Cambria2El Habib Nfaoui3Computer Science Department, LISAC Laboratory, Faculty of Sciences Dhar EL Mehraz (F.S.D.M), Sidi Mohamed Ben Abdellah University, Fez B.P. 1796, 30003, Fes-Atlas, Morocco; Corresponding author.Computer Science Department, LISAC Laboratory, Faculty of Sciences Dhar EL Mehraz (F.S.D.M), Sidi Mohamed Ben Abdellah University, Fez B.P. 1796, 30003, Fes-Atlas, MoroccoSchool of Computer Science and Engineering, Nanyang Technological University, SingaporeComputer Science Department, LISAC Laboratory, Faculty of Sciences Dhar EL Mehraz (F.S.D.M), Sidi Mohamed Ben Abdellah University, Fez B.P. 1796, 30003, Fes-Atlas, MoroccoThe global trend of individuals expressing their opinions on Twitter has led to a substantial number of user-generated reviews across various brands, products, and services. As a result, there is a growing need for automated systems capable of analyzing and interpreting this extensive content. In response, reputation generation systems have been developed to extract valuable insights from both textual and numerical reviews. However, many of these systems have significant limitations. Firstly, most of them are limited to processing English text, which poses a barrier for analyzing reviews in other languages. Also, they are incapable of handling immediate data influx, they often fall short in providing up-to-date and accurate reputation assessments. Therefore, we propose a two-phase system for generating accurate reputation values. In the first phase, data preparation, the system incorporates review translation to English, spam filtering, and sarcasm detection to address limitations of language processing and enhance data quality. This prepares the data for the second phase, reputation generation, which utilizes state-of-the-art, aspect-based sentiment analysis techniques, offering a novel approach to calculating reputation by considering specific aspects of products or services. Experimental results conducted on multiple datasets show the efficacy of the proposed system compared with previous reputation generation systems.http://www.sciencedirect.com/science/article/pii/S2590123025006966Aspect-based sentiment analysisDecision-makingReputation generationE-commerce |
| spellingShingle | Achraf Boumhidi Abdessamad Benlahbib Erik Cambria El Habib Nfaoui Periodic insight: Multilingual reputation generation system through daily opinion mining analysis Results in Engineering Aspect-based sentiment analysis Decision-making Reputation generation E-commerce |
| title | Periodic insight: Multilingual reputation generation system through daily opinion mining analysis |
| title_full | Periodic insight: Multilingual reputation generation system through daily opinion mining analysis |
| title_fullStr | Periodic insight: Multilingual reputation generation system through daily opinion mining analysis |
| title_full_unstemmed | Periodic insight: Multilingual reputation generation system through daily opinion mining analysis |
| title_short | Periodic insight: Multilingual reputation generation system through daily opinion mining analysis |
| title_sort | periodic insight multilingual reputation generation system through daily opinion mining analysis |
| topic | Aspect-based sentiment analysis Decision-making Reputation generation E-commerce |
| url | http://www.sciencedirect.com/science/article/pii/S2590123025006966 |
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