{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,13]],"date-time":"2026-02-13T17:16:21Z","timestamp":1771002981686,"version":"3.50.1"},"reference-count":22,"publisher":"SAGE Publications","issue":"2","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JCM"],"published-print":{"date-parts":[[2021,5,6]]},"abstract":"<jats:p>OBJECTIVE: With Sina Weibo data as the background, support vector machine (SVM) and k-nearest neighbor (KNN) method are used to predict and analyze the user\u2019s micro-blog emotion and related behavior in social network, hoping to obtain rich potential business value. METHODS: First, the API interface of Sina Weibo is utilized to obtain the information of users in Sina Weibo; then, the Excel software is utilized to sort and analyze the extracted data to extract the features of micro- blogs posted by users. Second, SVM and KNN algorithms are utilized to calculate the weighted average and propose a hybrid multi-classifier-based Mixed Classifier Emotion Prediction Model (MCEPM). Finally, through the evaluation criteria, including precision (P), recall rate (R), and harmonic average (F1), the specific experimental results of SVM and KNN weight coefficients are compared with the prediction results of MCEPM. RESULTS: The prediction effect of MCEPM is associated with the weight coefficients of SVM and KNN. If the weight coefficients of SVM and KNN are 0.6 and 0.4, the prediction effect of MCEPM will be optimal. Comprehensive analysis shows that the MCEPM model can balance the prediction results of the positive and negative samples of the two classifiers. CONCLUSION: MCEPM model is superior to other algorithms in micro-blog emotion prediction, which can help enterprises analyze users\u2019 product inclination and provide accurate customer service requirements for enterprises.<\/jats:p>","DOI":"10.3233\/jcm-204613","type":"journal-article","created":{"date-parts":[[2020,9,22]],"date-time":"2020-09-22T19:18:44Z","timestamp":1600802324000},"page":"435-447","source":"Crossref","is-referenced-by-count":5,"title":["Data mining and social networks processing method based on support vector machine and k-nearest neighbor"],"prefix":"10.1177","volume":"21","author":[{"given":"Youli","family":"Lu","sequence":"first","affiliation":[{"name":"School of Information and Communication Engineering, Hunan Institute of Science and Technology, Yueyang, Hunan, China"}]},{"given":"Jintong","family":"Li","sequence":"additional","affiliation":[{"name":"Nanhu College, Hunan Institute of Science and Technology, Yueyang 414006, Hunan, China"}]},{"given":"Zhihe","family":"Yang","sequence":"additional","affiliation":[{"name":"School of Information and Communication Engineering, Hunan Institute of Science and Technology, Yueyang, Hunan, China"}]},{"given":"Xianfeng","family":"Ou","sequence":"additional","affiliation":[{"name":"School of Information and Communication Engineering, Hunan Institute of Science and Technology, Yueyang, Hunan, China"}]},{"given":"Wenwu","family":"Xie","sequence":"additional","affiliation":[{"name":"School of Information and Communication Engineering, Hunan Institute of Science and Technology, Yueyang, Hunan, China"}]}],"member":"179","reference":[{"issue":"1","key":"10.3233\/JCM-204613_ref1","first-page":"1","article-title":"Thematic series on social network analysis and mining","volume":"10","author":"Rodrigo","year":"2019","journal-title":"Journal of Internet Services and Applications"},{"issue":"3","key":"10.3233\/JCM-204613_ref2","doi-asserted-by":"crossref","first-page":"535","DOI":"10.1109\/TCSS.2019.2915543","article-title":"User rating classification via deep belief network learning and sentiment analysis","volume":"6","author":"Chen","year":"2019","journal-title":"IEEE Transactions on Computational Social Systems"},{"issue":"9","key":"10.3233\/JCM-204613_ref3","first-page":"2048","article-title":"Extended multi-modality features and deep learning based microblog short text sentiment analysis","volume":"39","author":"Sun","year":"2017","journal-title":"Journal of Electronics & Information Technology"},{"issue":"1","key":"10.3233\/JCM-204613_ref4","first-page":"1293","article-title":"CS-1-SVM: Improved one-class svm for detecting API abuse on open network service","volume":"12","author":"Xie","year":"2015","journal-title":"Journal of Computational and Theoretical Nanoscience"},{"issue":"2","key":"10.3233\/JCM-204613_ref5","doi-asserted-by":"crossref","first-page":"16","DOI":"10.14569\/IJACSA.2016.070203","article-title":"The SVM classifier based on the modified particle swarm optimization","volume":"7","author":"Demidova","year":"2016","journal-title":"International Journal of Advanced Computer Science & Applications"},{"issue":"11","key":"10.3233\/JCM-204613_ref6","doi-asserted-by":"crossref","first-page":"1","DOI":"10.23919\/JCC.2019.11.001","article-title":"Finding the hidden hands: a case study of detecting organized posters and promoters in SINA weibo","volume":"12","author":"Wang","year":"2015","journal-title":"China Communications"},{"issue":"5","key":"10.3233\/JCM-204613_ref7","doi-asserted-by":"crossref","first-page":"1633","DOI":"10.1109\/TLA.2015.7112025","article-title":"SVM and ANN application to multivariate pattern recognition using scatter data","volume":"13","author":"Chinas","year":"2015","journal-title":"IEEE Latin America Transactions"},{"key":"10.3233\/JCM-204613_ref8","doi-asserted-by":"crossref","unstructured":"W. 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