Comparative relation mining of customer reviews based on a hybrid CSR method
Online reviews contain comparative opinions that reveal the competitive relationships of related products, help identify the competitiveness of products in the marketplace, and influence consumers’ purchasing choices. The Class Sequence Rule (CSR) method, which is previously commonly used to identif...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2023-12-01
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| Series: | Connection Science |
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| Online Access: | http://dx.doi.org/10.1080/09540091.2023.2251717 |
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| _version_ | 1850244854695591936 |
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| author | Song Gao Hongwei Wang Yuanjun Zhu Jiaqi Liu Ou Tang |
| author_facet | Song Gao Hongwei Wang Yuanjun Zhu Jiaqi Liu Ou Tang |
| author_sort | Song Gao |
| collection | DOAJ |
| description | Online reviews contain comparative opinions that reveal the competitive relationships of related products, help identify the competitiveness of products in the marketplace, and influence consumers’ purchasing choices. The Class Sequence Rule (CSR) method, which is previously commonly used to identify the comparative relations of reviews, suffers from low recognition efficiency and inaccurate generation of rules. In this paper, we improve on the CSR method by proposing a hybrid CSR method, which utilises dependency relations and the part-of-speech to identify frequent sequence patterns in customer reviews, which can reduce manual intervention and reinforce sequence rules in the relation mining process. Such a method outperforms CSR and other CSR-based models with an F-value of 84.67%. In different experiments, we find that the method is characterised by less time-consuming and efficient in generating sequence patterns, as the dependency direction helps to reduce the sequence length. In addition, this method also performs well in implicit relation mining for extracting comparative information that lacks obvious rules. In this study, the optimal CSR method is applied to automatically capture the deeper features of comparative relations, thus improving the process of recognising explicit and implicit comparative relations. |
| format | Article |
| id | doaj-art-ab52f79ba9ef4c8c9cdcd56d30dbf05f |
| institution | OA Journals |
| issn | 0954-0091 1360-0494 |
| language | English |
| publishDate | 2023-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Connection Science |
| spelling | doaj-art-ab52f79ba9ef4c8c9cdcd56d30dbf05f2025-08-20T01:59:38ZengTaylor & Francis GroupConnection Science0954-00911360-04942023-12-0135110.1080/09540091.2023.22517172251717Comparative relation mining of customer reviews based on a hybrid CSR methodSong Gao0Hongwei Wang1Yuanjun Zhu2Jiaqi Liu3Ou Tang4China Information Technology Security Evaluation Center, Beijing, People’s Republic of ChinaSchool of Economics & ManagementSchool of Economics & ManagementSchool of Economics & ManagementLinköping UniversityOnline reviews contain comparative opinions that reveal the competitive relationships of related products, help identify the competitiveness of products in the marketplace, and influence consumers’ purchasing choices. The Class Sequence Rule (CSR) method, which is previously commonly used to identify the comparative relations of reviews, suffers from low recognition efficiency and inaccurate generation of rules. In this paper, we improve on the CSR method by proposing a hybrid CSR method, which utilises dependency relations and the part-of-speech to identify frequent sequence patterns in customer reviews, which can reduce manual intervention and reinforce sequence rules in the relation mining process. Such a method outperforms CSR and other CSR-based models with an F-value of 84.67%. In different experiments, we find that the method is characterised by less time-consuming and efficient in generating sequence patterns, as the dependency direction helps to reduce the sequence length. In addition, this method also performs well in implicit relation mining for extracting comparative information that lacks obvious rules. In this study, the optimal CSR method is applied to automatically capture the deeper features of comparative relations, thus improving the process of recognising explicit and implicit comparative relations.http://dx.doi.org/10.1080/09540091.2023.2251717comparative relation miningclass sequence ruledependency parsingimplicit comparative relation |
| spellingShingle | Song Gao Hongwei Wang Yuanjun Zhu Jiaqi Liu Ou Tang Comparative relation mining of customer reviews based on a hybrid CSR method Connection Science comparative relation mining class sequence rule dependency parsing implicit comparative relation |
| title | Comparative relation mining of customer reviews based on a hybrid CSR method |
| title_full | Comparative relation mining of customer reviews based on a hybrid CSR method |
| title_fullStr | Comparative relation mining of customer reviews based on a hybrid CSR method |
| title_full_unstemmed | Comparative relation mining of customer reviews based on a hybrid CSR method |
| title_short | Comparative relation mining of customer reviews based on a hybrid CSR method |
| title_sort | comparative relation mining of customer reviews based on a hybrid csr method |
| topic | comparative relation mining class sequence rule dependency parsing implicit comparative relation |
| url | http://dx.doi.org/10.1080/09540091.2023.2251717 |
| work_keys_str_mv | AT songgao comparativerelationminingofcustomerreviewsbasedonahybridcsrmethod AT hongweiwang comparativerelationminingofcustomerreviewsbasedonahybridcsrmethod AT yuanjunzhu comparativerelationminingofcustomerreviewsbasedonahybridcsrmethod AT jiaqiliu comparativerelationminingofcustomerreviewsbasedonahybridcsrmethod AT outang comparativerelationminingofcustomerreviewsbasedonahybridcsrmethod |