{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,2]],"date-time":"2026-05-02T06:43:42Z","timestamp":1777704222326,"version":"3.51.4"},"reference-count":14,"publisher":"SAGE Publications","issue":"3","license":[{"start":{"date-parts":[[2018,6,14]],"date-time":"2018-06-14T00:00:00Z","timestamp":1528934400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Journal of Intelligent &amp; Fuzzy Systems"],"published-print":{"date-parts":[[2018,10]]},"abstract":"<jats:p>Sentiment analysis mainly studies the emotional tendencies of texts from grammar, semantic rules and other aspects. The texts from social network are characterized by less words, irregular grammar, data noise and so on, which have increased the difficulty of emotion analysis. In order to improve the performance of machine learning in sentiment analysis, this study proposed the Majority Decision Algorithm to classify the emotional tendentious of the text in WeChat, combined the characteristics of five classifiers and integrated the classification results of five classifiers, eventually the text can be classified in WeChat. Firstly, this study utilized the BlueStacks to crawl the cache of WeChat Moment developed by Tencent company. Secondly, the cache was processed by Python to get the WeChat dataset. After the Chinese word segmentation, data cleaning and segmentation, the sentiment classification experiment were carried out using different classifiers. Finally, a Majority Decision Algorithm composed of five classifiers was established. It included, Naive Bayes (sklearn), Naive Bayes (SnowNLP), SVM (linear), SVM (RBF) and SGD. Then, the comparison was carried out between the performance of the algorithm and the five classifiers. Results show that the precision rates of the five classifiers are 0.8598, 0.8154, 0.8511, 0.8739 and 0.8678; the recall rates are 0.8544, 0.8482, 0.9380, 0.9226 and 0.9349; F1 scores are 0.8571, 0.8315, 0.8924, 0.8975 and 0.9001, respectively. The algorithm of the Precision rate, Recall rate and F1 score were 0.8804, 0.9349 and 0.9069, respectively, indicating that algorithm in current study significantly improved the performance, which can be effectively applied into the new text form of WeChat Moment. The study can provide theoretical reference for sentiment classification of Chinese text based on machine learning.<\/jats:p>","DOI":"10.3233\/jifs-169653","type":"journal-article","created":{"date-parts":[[2018,6,15]],"date-time":"2018-06-15T13:18:36Z","timestamp":1529068716000},"page":"2975-2984","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":20,"title":["Research on the Majority Decision Algorithm based on WeChat sentiment classification"],"prefix":"10.1177","volume":"35","author":[{"given":"Sheng Tai","family":"Zhang","sequence":"first","affiliation":[{"name":"School of Economics and Management, Beijing University of Posts and Telecommunications, Beijing, China"}]},{"given":"Fei Fei","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Economics and Management, Beijing University of Posts and Telecommunications, Beijing, China"}]},{"given":"Fan","family":"Duo","sequence":"additional","affiliation":[{"name":"School of Information and Communication Engineering, Beijing University of Posts and Telecommunications, Beijing, China"}]},{"given":"Ju Liang","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Economics and Management, Beijing University of Posts and Telecommunications, Beijing, China"}]}],"member":"179","published-online":{"date-parts":[[2018,6,14]]},"reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2017.03.071"},{"key":"e_1_3_2_3_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.dss.2016.06.013"},{"key":"e_1_3_2_4_2","first-page":"9","article-title":"Sarcasm detection in microblogs using Na\u00efve Bayes and fuzzy clustering","volume":"48","author":"Mukherjee S.","year":"2016","unstructured":"MukherjeeS. and BalaP.K., Sarcasm detection in microblogs using Na\u00efve Bayes and fuzzy clustering, Technology in Society48 ( (2016), 9\u201327.","journal-title":"Technology in Society"},{"key":"e_1_3_2_5_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2017.01.079"},{"key":"e_1_3_2_6_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2008.07.035"},{"key":"e_1_3_2_7_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2016.11.022"},{"key":"e_1_3_2_8_2","doi-asserted-by":"publisher","DOI":"10.3724\/SP.J.1001.2010.03832"},{"key":"e_1_3_2_9_2","doi-asserted-by":"publisher","DOI":"10.1007\/s13278-014-0181-9"},{"key":"e_1_3_2_10_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2016.02.002"},{"key":"e_1_3_2_11_2","first-page":"328","article-title":"GU-MLT-LT: Sentiment analysis of shortmessages using linguistic features and stochastic gradient descent","volume":"2","author":"G\u00fcnther T.","year":"2013","unstructured":"G\u00fcntherT. and FurrerL., GU-MLT-LT: Sentiment analysis of shortmessages using linguistic features and stochastic gradient descent, Joint Conference on Lexical and Computational Semantics2(C) (2013), 328\u2013332.","journal-title":"Joint Conference on Lexical and Computational Semantics"},{"key":"e_1_3_2_12_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-662-46578-3_74"},{"key":"e_1_3_2_13_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.dss.2014.10.004"},{"issue":"1","key":"e_1_3_2_14_2","first-page":"67","article-title":"Multi-strategy approach for fine-grained sentiment analysis of Chinese microblogy","volume":"50","author":"Ouyang C.","year":"2014","unstructured":"OuyangC., YangX., LeiL.et al., Multi-strategy approach for fine-grained sentiment analysis of Chinese microblogy, Acta Scientiarum Naturalium Universitatis Pekinensis50(1) (2014), 67\u201372.","journal-title":"Acta Scientiarum Naturalium Universitatis Pekinensis"},{"key":"e_1_3_2_15_2","doi-asserted-by":"publisher","DOI":"10.1007\/BF00994018"}],"container-title":["Journal of Intelligent &amp; 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