ALSEE: a framework for attribute-level sentiment element extraction towards product reviews
Attribute-level sentiment element extraction aims to obtain the word pair < opinion target, opinion word > from texts, which mainly obtain fine-grained evaluation information in the attribute level. Due to the information fragmentation and semantic sparseness of product reviews, it is difficul...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2022-12-01
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| Series: | Connection Science |
| Subjects: | |
| Online Access: | http://dx.doi.org/10.1080/09540091.2021.1981825 |
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| _version_ | 1850245822654971904 |
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| author | Hanqing Xu Shunxiang Zhang Guangli Zhu Haiyang Zhu |
| author_facet | Hanqing Xu Shunxiang Zhang Guangli Zhu Haiyang Zhu |
| author_sort | Hanqing Xu |
| collection | DOAJ |
| description | Attribute-level sentiment element extraction aims to obtain the word pair < opinion target, opinion word > from texts, which mainly obtain fine-grained evaluation information in the attribute level. Due to the information fragmentation and semantic sparseness of product reviews, it is difficult to capture more comprehensive local information from unstructured texts, which leads to the incorrect extraction of some word pairs. Aimed at the problem, this paper proposes a framework for Attribute-Level Sentiment Element Extraction (ALSEE) towards product reviews. Firstly, a small amount of sample data is selected by random sampling, and multiple features (including part of speech, word distance, dependency relationship and semantic role) which are labelled. The labelled data are used as the training set. Then, the Condition Random Field (CRF) model is applied to extract opinion targets (OT) and opinion words (OW). The self-training strategy is used to achieve the semi-supervised learning of CRF model through iterative training. Finally, target-opinion word pairs with modifying relationship are obtained by dependency parsing. Compared with the existing methods, the proposed framework can effectively extract attribute-level sentiment elements though experimental results. |
| format | Article |
| id | doaj-art-0d0fe8eda257415ea145a0bc07e8c9d4 |
| institution | OA Journals |
| issn | 0954-0091 1360-0494 |
| language | English |
| publishDate | 2022-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Connection Science |
| spelling | doaj-art-0d0fe8eda257415ea145a0bc07e8c9d42025-08-20T01:59:21ZengTaylor & Francis GroupConnection Science0954-00911360-04942022-12-0134120522310.1080/09540091.2021.19818251981825ALSEE: a framework for attribute-level sentiment element extraction towards product reviewsHanqing Xu0Shunxiang Zhang1Guangli Zhu2Haiyang Zhu3School of Computer Science and Engineering, Anhui University of Science & TechnologySchool of Computer Science and Engineering, Anhui University of Science & TechnologySchool of Computer Science and Engineering, Anhui University of Science & TechnologySchool of Computer Science and Engineering, Anhui University of Science & TechnologyAttribute-level sentiment element extraction aims to obtain the word pair < opinion target, opinion word > from texts, which mainly obtain fine-grained evaluation information in the attribute level. Due to the information fragmentation and semantic sparseness of product reviews, it is difficult to capture more comprehensive local information from unstructured texts, which leads to the incorrect extraction of some word pairs. Aimed at the problem, this paper proposes a framework for Attribute-Level Sentiment Element Extraction (ALSEE) towards product reviews. Firstly, a small amount of sample data is selected by random sampling, and multiple features (including part of speech, word distance, dependency relationship and semantic role) which are labelled. The labelled data are used as the training set. Then, the Condition Random Field (CRF) model is applied to extract opinion targets (OT) and opinion words (OW). The self-training strategy is used to achieve the semi-supervised learning of CRF model through iterative training. Finally, target-opinion word pairs with modifying relationship are obtained by dependency parsing. Compared with the existing methods, the proposed framework can effectively extract attribute-level sentiment elements though experimental results.http://dx.doi.org/10.1080/09540091.2021.1981825product reviewssentiment elementssemi-supervised learningcrfself-training |
| spellingShingle | Hanqing Xu Shunxiang Zhang Guangli Zhu Haiyang Zhu ALSEE: a framework for attribute-level sentiment element extraction towards product reviews Connection Science product reviews sentiment elements semi-supervised learning crf self-training |
| title | ALSEE: a framework for attribute-level sentiment element extraction towards product reviews |
| title_full | ALSEE: a framework for attribute-level sentiment element extraction towards product reviews |
| title_fullStr | ALSEE: a framework for attribute-level sentiment element extraction towards product reviews |
| title_full_unstemmed | ALSEE: a framework for attribute-level sentiment element extraction towards product reviews |
| title_short | ALSEE: a framework for attribute-level sentiment element extraction towards product reviews |
| title_sort | alsee a framework for attribute level sentiment element extraction towards product reviews |
| topic | product reviews sentiment elements semi-supervised learning crf self-training |
| url | http://dx.doi.org/10.1080/09540091.2021.1981825 |
| work_keys_str_mv | AT hanqingxu alseeaframeworkforattributelevelsentimentelementextractiontowardsproductreviews AT shunxiangzhang alseeaframeworkforattributelevelsentimentelementextractiontowardsproductreviews AT guanglizhu alseeaframeworkforattributelevelsentimentelementextractiontowardsproductreviews AT haiyangzhu alseeaframeworkforattributelevelsentimentelementextractiontowardsproductreviews |