Sentiment classification of Chinese Weibo based on extended sentiment dictionary and organisational structure of comments
Sentiment classification can provide the decision support of social applications such as trend judgment, public opinion monitoring, etc. However, the accuracy of sentiment classification for Chinese Weibo is still not satisfactory due to the complexity of Chinese. In addition, affected by the differ...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis Group
2022-12-01
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| Series: | Connection Science |
| Subjects: | |
| Online Access: | http://dx.doi.org/10.1080/09540091.2021.2006146 |
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| _version_ | 1850232687412903936 |
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| author | Zhongliang Wei Wenjuan Liu Guangli Zhu Shunxiang Zhang Meng-Yen Hsieh |
| author_facet | Zhongliang Wei Wenjuan Liu Guangli Zhu Shunxiang Zhang Meng-Yen Hsieh |
| author_sort | Zhongliang Wei |
| collection | DOAJ |
| description | Sentiment classification can provide the decision support of social applications such as trend judgment, public opinion monitoring, etc. However, the accuracy of sentiment classification for Chinese Weibo is still not satisfactory due to the complexity of Chinese. In addition, affected by the different organisational structure levels, the sentiment tendency of fewer Weibo Comments may be judged to be the opposite. To solve the problem above, this paper presents a Chinese sentiment classification model based on extended sentiment dictionary and organisational structure of comments. First, the sentiment dictionary can be extended by using seven dictionaries, which include the base sentiment dictionary and six additional dictionaries. Then, the sets of rules are constructed, which include inter-sentence rules and organisational structure rules. Finally, comments on three hot topics are crawled and used to make the data sets for sentiment calculation. Accordingly, based on the result of sentiment calculation, sentiment classification is completed. The effectiveness of the proposed model is verified through comparison experiments, and the experimental results are also discussed. |
| format | Article |
| id | doaj-art-043d86b312cd4577b242610c8400b2cc |
| 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-043d86b312cd4577b242610c8400b2cc2025-08-20T02:03:06ZengTaylor & Francis GroupConnection Science0954-00911360-04942022-12-0134140942810.1080/09540091.2021.20061462006146Sentiment classification of Chinese Weibo based on extended sentiment dictionary and organisational structure of commentsZhongliang Wei0Wenjuan Liu1Guangli Zhu2Shunxiang Zhang3Meng-Yen Hsieh4Anhui University of Science and TechnologyAnhui University of Science and TechnologyAnhui University of Science and TechnologyAnhui University of Science and TechnologyProvidence UniversitySentiment classification can provide the decision support of social applications such as trend judgment, public opinion monitoring, etc. However, the accuracy of sentiment classification for Chinese Weibo is still not satisfactory due to the complexity of Chinese. In addition, affected by the different organisational structure levels, the sentiment tendency of fewer Weibo Comments may be judged to be the opposite. To solve the problem above, this paper presents a Chinese sentiment classification model based on extended sentiment dictionary and organisational structure of comments. First, the sentiment dictionary can be extended by using seven dictionaries, which include the base sentiment dictionary and six additional dictionaries. Then, the sets of rules are constructed, which include inter-sentence rules and organisational structure rules. Finally, comments on three hot topics are crawled and used to make the data sets for sentiment calculation. Accordingly, based on the result of sentiment calculation, sentiment classification is completed. The effectiveness of the proposed model is verified through comparison experiments, and the experimental results are also discussed.http://dx.doi.org/10.1080/09540091.2021.2006146sentiment dictionaryweibo hot topicssentiment classificationorganisational structure |
| spellingShingle | Zhongliang Wei Wenjuan Liu Guangli Zhu Shunxiang Zhang Meng-Yen Hsieh Sentiment classification of Chinese Weibo based on extended sentiment dictionary and organisational structure of comments Connection Science sentiment dictionary weibo hot topics sentiment classification organisational structure |
| title | Sentiment classification of Chinese Weibo based on extended sentiment dictionary and organisational structure of comments |
| title_full | Sentiment classification of Chinese Weibo based on extended sentiment dictionary and organisational structure of comments |
| title_fullStr | Sentiment classification of Chinese Weibo based on extended sentiment dictionary and organisational structure of comments |
| title_full_unstemmed | Sentiment classification of Chinese Weibo based on extended sentiment dictionary and organisational structure of comments |
| title_short | Sentiment classification of Chinese Weibo based on extended sentiment dictionary and organisational structure of comments |
| title_sort | sentiment classification of chinese weibo based on extended sentiment dictionary and organisational structure of comments |
| topic | sentiment dictionary weibo hot topics sentiment classification organisational structure |
| url | http://dx.doi.org/10.1080/09540091.2021.2006146 |
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