{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,26]],"date-time":"2025-12-26T08:44:32Z","timestamp":1766738672915,"version":"3.40.5"},"reference-count":21,"publisher":"World Scientific Pub Co Pte Ltd","issue":"06n07","funder":[{"name":"the Provincial Quality Engineering Project of Anhui Province","award":["2021xnfzxm019"],"award-info":[{"award-number":["2021xnfzxm019"]}]},{"name":"the Quality Engineering Project of Anhui Jianzhu University","award":["2021xnfz04"],"award-info":[{"award-number":["2021xnfz04"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Int. J. Found. Comput. Sci."],"published-print":{"date-parts":[[2022,9]]},"abstract":"<jats:p> It is of great significance for individuals, enterprises, and government departments to analyze and excavate the sentiment in the comments. Many deep learning models are used for text sentiment analysis, and the BiTCN model has good efficacy on sentiment analysis. However, in the actual semantic expression, the contribution of each word to the sentiment tendency is different, BiTCN treats it fairly and does not pay more attention to the key sentiment words. For this problem, a sentiment analysis model based on the BiTCN-Attention is proposed in this paper. The Self-Attention mechanism and Multi-Head Self-Attention mechanism are added to BiTCN respectively to form BiTCN-SA and BiTCN-MHSA, which improve the weight of sentiment words and the accuracy of feature extraction, to increase the effect of sentiment analysis. The experimental results show that the model accuracies of BiTCN-SA and BiTCN-MHSA in the JingDong commodity review data set are 3.96% and 2.41% higher than that of BiTCN, respectively. In the comment data set of DianPing, the accuracy of BiTCN-SA and BiTCN-MHSA improved by 4.62% and 3.49%, respectively, compared with that of BiTCN. <\/jats:p>","DOI":"10.1142\/s0129054122420138","type":"journal-article","created":{"date-parts":[[2022,8,11]],"date-time":"2022-08-11T08:46:43Z","timestamp":1660207603000},"page":"755-770","source":"Crossref","is-referenced-by-count":8,"title":["Sentiment Analysis of Chinese Reviews Based on BiTCN-Attention Model"],"prefix":"10.1142","volume":"33","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4802-0356","authenticated-orcid":false,"given":"Jiajing","family":"Zhang","sequence":"first","affiliation":[{"name":"School of Electronics and Information Engineering, Anhui Jianzhu University, Hefei 230601, China"}]},{"given":"Tingting","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Electronics and Information Engineering, Anhui Jianzhu University, Hefei 230601, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5693-2195","authenticated-orcid":false,"given":"Jinlan","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Mechanical and Electrical Engineering, Anhui Jianzhu University, Hefei 230601, China"}]}],"member":"219","published-online":{"date-parts":[[2022,8,11]]},"reference":[{"issue":"02","key":"S0129054122420138BIB001","first-page":"205","volume":"46","author":"Chen Ji","year":"2022","journal-title":"Journal of Nanjing University of Science and Technology"},{"issue":"06","key":"S0129054122420138BIB002","first-page":"1255","volume":"59","author":"Xibin Jia","year":"2022","journal-title":"Journal of Computer Research and Development"},{"key":"S0129054122420138BIB003","doi-asserted-by":"publisher","DOI":"10.1007\/s10489-022-03389-4"},{"first-page":"1746","volume-title":"Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing","author":"Kim Y.","key":"S0129054122420138BIB004"},{"volume-title":"6th International Conference on Learning Representations","year":"2018","author":"Bai S.","key":"S0129054122420138BIB006"},{"key":"S0129054122420138BIB007","first-page":"253","volume-title":"Proceedings of the 8th International Joint Conference on Natural Language Processing","author":"Zhang Y.","year":"2017"},{"key":"S0129054122420138BIB008","doi-asserted-by":"publisher","DOI":"10.1207\/s15516709cog1402_1"},{"issue":"1","key":"S0129054122420138BIB009","first-page":"36","volume":"5","author":"Tai K. 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