{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,1]],"date-time":"2026-02-01T00:38:57Z","timestamp":1769906337542,"version":"3.49.0"},"reference-count":29,"publisher":"Institute of Electronics, Information and Communications Engineers (IEICE)","issue":"10","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEICE Trans. Inf. &amp; Syst."],"published-print":{"date-parts":[[2017]]},"DOI":"10.1587\/transinf.2016edp7473","type":"journal-article","created":{"date-parts":[[2017,9,30]],"date-time":"2017-09-30T22:24:21Z","timestamp":1506810261000},"page":"2515-2525","source":"Crossref","is-referenced-by-count":12,"title":["Emotional Community Detection in Social Network"],"prefix":"10.1587","volume":"E100.D","author":[{"given":"Jiang","family":"ZHU","sequence":"first","affiliation":[{"name":"School of Computer Science, Beijing University of Posts and Telecommunications"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bai","family":"WANG","sequence":"additional","affiliation":[{"name":"School of Computer Science, Beijing University of Posts and Telecommunications"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bin","family":"WU","sequence":"additional","affiliation":[{"name":"School of Computer Science, Beijing University of Posts and Telecommunications"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Weiyu","family":"ZHANG","sequence":"additional","affiliation":[{"name":"School of Computer Science, Beijing University of Posts and Telecommunications"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"532","reference":[{"key":"1","doi-asserted-by":"crossref","unstructured":"[1] A. Java, X. Song, T. Finin, and B. Tseng, \u201cWhy we twitter: understanding microblogging usage and communities,\u201d Proceedings of the 9th WebKDD and 1st SNA-KDD 2007 workshop on Web mining and social network analysis, August 12-15, 2007, San Jose, CA, USA. ACM, pp.56-65, 2007. 10.1145\/1348549.1348556","DOI":"10.1145\/1348549.1348556"},{"key":"2","doi-asserted-by":"publisher","unstructured":"[2] J. Zhu, B. Wang, and B. Wu, \u201cSocial network users clustering based on multivariate time series of emotional behavior,\u201d The Journal of China Universities of Posts and Telecommunications, vol.21, no.2, pp.21-31, 2014. 10.1016\/s1005-8885(14)60282-x","DOI":"10.1016\/S1005-8885(14)60282-X"},{"key":"3","unstructured":"[3] A. Clauset, M.E.J. Newman, and C. Moore, \u201cFinding community structure in very large networks,\u201d Physical review E, vol.70, no.6, 066111, 2004. 10.1103\/physreve.70.066111"},{"key":"4","doi-asserted-by":"publisher","unstructured":"[4] V.D. Blondel, J.-L. Guillaume, R. Lambiotte, and E. Lefebvre, \u201cFast unfolding of communities in large networks,\u201d Journal of statistical mechanics: theory and experiment, vol.2008, no.10, P10008, 2008. 10.1088\/1742-5468\/2008\/10\/p10008","DOI":"10.1088\/1742-5468\/2008\/10\/P10008"},{"key":"5","doi-asserted-by":"crossref","unstructured":"[5] H. Qin, T. Liu, and Y. Ma, \u201cMining user&apos;s real social circle in microblog,\u201d Proceedings of the 2012 International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2012), IEEE Computer Society, pp.348-352, 2012. 10.1109\/asonam.2012.64","DOI":"10.1109\/ASONAM.2012.64"},{"key":"6","doi-asserted-by":"crossref","unstructured":"[6] Y. Huang, W. Hou, X. Li, and S. Li, \u201cAn effective community detection algorithm of the social networks,\u201d Information Science and Technology (ICIST), 2013 International Conference on, IEEE, pp.824-827, 2013. 10.1109\/icist.2013.6747668","DOI":"10.1109\/ICIST.2013.6747668"},{"key":"7","doi-asserted-by":"crossref","unstructured":"[7] A. Hajibagheri, H. Alvari, A. Hamzeh, and S. Hashemi, \u201cSocial networks community detection using the Shapley value,\u201d Artificial Intelligence and Signal Processing (AISP), 2012 16th CSI International Symposium on, IEEE, pp.222-227, 2012. 10.1109\/aisp.2012.6313748","DOI":"10.1109\/AISP.2012.6313748"},{"key":"8","doi-asserted-by":"publisher","unstructured":"[8] X. Xiong, G. Zhou, X. Niu, Y. Huang, and K. Xu, \u201cRemodeling the network for microgroup detection on microblog,\u201d Knowledge and information systems, vol.39, no.3, pp.643-665, 2014. 10.1007\/s10115-013-0626-x","DOI":"10.1007\/s10115-013-0626-x"},{"key":"9","unstructured":"[9] A. Vakali and K. Kafetsios, \u201cEmotion aware clustering analysis as a tool for Web 2.0 communities detection: Implications for curriculum development,\u201d Proceedings of the 22nd International World Wide Web Conference, W3C, 2013."},{"key":"10","doi-asserted-by":"crossref","unstructured":"[10] T.-A. Hoang, W.W. Cohen, E.-P. Lim, D. Pierce, and D.P. Redlawsk, \u201cPolitics, sharing and emotion in microblogs,\u201d Proceedings of the 2013 IEEE\/ACM International Conference on Advances in Social Networks Analysis and Mining, ACM, pp.282-289, 2013. 10.1145\/2492517.2492554","DOI":"10.1145\/2492517.2492554"},{"key":"11","doi-asserted-by":"publisher","unstructured":"[11] M. Mitrovi\u0107, G. Paltoglou, and B. Tadi\u0107, \u201cNetworks and emotion-driven user communities at popular blogs,\u201d The European Physical Journal B, vol.77, no.4, pp.597-609, 2010. 10.1140\/epjb\/e2010-00279-x","DOI":"10.1140\/epjb\/e2010-00279-x"},{"key":"12","unstructured":"[12] M.M. Bradley and P.J. Lang, \u201cAffective norms for English words (ANEW): Instruction manual and affective ratings,\u201d Technical Report C-1, The Center for Research in Psychophysiology, University of Florida, 1999."},{"key":"13","doi-asserted-by":"crossref","unstructured":"[13] Y. Shen, S. Li, L. Zheng, X. Ren, and X. Cheng, \u201cEmotion mining research on micro-blog,\u201d SWS&apos;09, 1st IEEE Symposium on, June 21-26 2009, MADISON, WI, IEEE, pp.71-75, 2009. 10.1109\/sws.2009.5271711","DOI":"10.1109\/SWS.2009.5271711"},{"key":"14","unstructured":"[14] J. Bollen, H. Mao, and A. Pepe, \u201cModeling public mood and emotion: Twitter sentiment and socio-economic phenomena,\u201dProceedings of the Fifth International AAAI Conference on Weblogs and Social Media, AAAI, 2011."},{"key":"15","doi-asserted-by":"publisher","unstructured":"[15] J. Bollen, H. Mao, and X. Zeng, \u201cTwitter mood predicts the stock market,\u201d Journal of Computational Science, vol.2, no.1, pp.1-8, 2011. 10.1016\/j.jocs.2010.12.007","DOI":"10.1016\/j.jocs.2010.12.007"},{"key":"16","unstructured":"[16] J. Bollen, H. Mao, and A. Pepe, \u201cDetermining the Public Mood State by Analysis of Microblogging Posts,\u201d Proceedings of the Alife XII Conference, 2010, Odense, Denmark, pp.667-668, 2010."},{"key":"17","doi-asserted-by":"publisher","unstructured":"[17] A. Singhal and D.E. Seborg, \u201cClustering multivariate time-series data,\u201d Journal of chemometrics, vol.19, no.8, pp.427-438, 2005. 10.1002\/cem.945","DOI":"10.1002\/cem.945"},{"key":"18","doi-asserted-by":"publisher","unstructured":"[18] M. McPherson, L. Smith-Lovin, and J.M. Cook, \u201cBirds of a feather: Homophily in social networks,\u201d Annual review of sociology, vol.27, no.1, pp.415-444, 2001. 10.1146\/annurev.soc.27.1.415","DOI":"10.1146\/annurev.soc.27.1.415"},{"key":"19","doi-asserted-by":"publisher","unstructured":"[19] J. Bollen, B. Gon\u00e7alves, G. Ruan, and H. Mao, \u201cHappiness is assortative in online social networks,\u201d Artificial life, vol.17, no.3, pp.237-251, 2011. 10.1162\/artl_a_00034","DOI":"10.1162\/artl_a_00034"},{"key":"20","unstructured":"[20] S. Kim, J.Y. Bak, and A.H. Oh, Do You Feel What I Feel? Social Aspects of Emotions in Twitter Conversations, ICWSM, 2012."},{"key":"21","doi-asserted-by":"crossref","unstructured":"[21] A.D.I. Kramer, J.E. Guillory, and J.T. Hancock, \u201cExperimental evidence of massive-scale emotional contagion through social networks,\u201d Proceedings of the National Academy of Sciences, vol.111, no.24, pp.8788-8790, 2014. 10.1073\/pnas.1320040111","DOI":"10.1073\/pnas.1320040111"},{"key":"22","doi-asserted-by":"crossref","unstructured":"[22] S. Tsugawa and H. Ohsaki, \u201cNegative messages spread rapidly and widely on social media,\u201d Proceedings of the 2015 ACM on Conference on Online Social Networks, ACM, pp.151-160, 2015. 10.1145\/2817946.2817962","DOI":"10.1145\/2817946.2817962"},{"key":"23","doi-asserted-by":"publisher","unstructured":"[23] E. Ferrara and Z. Yang, \u201cMeasuring emotional contagion in social media,\u201d PloS one, vol.10, no.11, e0142390, 2015. 10.1371\/journal.pone.0142390","DOI":"10.1371\/journal.pone.0142390"},{"key":"24","doi-asserted-by":"publisher","unstructured":"[24] E. Ferrara and Z. Yang, \u201cQuantifying the effect of sentiment on information diffusion in social media,\u201d PeerJ Computer Science, vol.1, e26, 2015. 10.7717\/peerj-cs.26","DOI":"10.7717\/peerj-cs.26"},{"key":"25","unstructured":"[25] J. Zhu, B. Wang, and B. Wu, \u201cAnalysison Sina Micro-Blogging Graded Emotional Behavior and Its Modeling and Simulation,\u201d Journal of Beijing University of Posts and Telecom, vol.36, no.6, pp.50-54, 2013."},{"key":"26","unstructured":"[26] D.M. Blei, A.Y. Ng, and M.I. Jordan, \u201cLatent dirichlet allocation,\u201d Journal of machine Learning research, pp.993-1022, Jan. 2003."},{"key":"27","doi-asserted-by":"publisher","unstructured":"[27] D.M. Endres and J.E. Schindelin, \u201cA new metric for probability distributions,\u201d IEEE Transactions on Information theory, vol.49, no.7, pp.1858-1860, 2003. 10.1109\/tit.2003.813506","DOI":"10.1109\/TIT.2003.813506"},{"key":"28","doi-asserted-by":"crossref","unstructured":"[28] Y. Kim, S.-W. Son, and H. Jeong, \u201cCommunity identification in directed networks,\u201d Complex sciences, Springer Berlin Heidelberg, vol.5, pp.2050-2053, 2009. 10.1007\/978-3-642-02469-6_81","DOI":"10.1007\/978-3-642-02469-6_81"},{"key":"29","doi-asserted-by":"publisher","unstructured":"[29] C.O. Dorso and A.D. Medus, \u201cCommunity Detection in Networks,\u201d International Journal of Bifurcation and Chaos, vol.20, no.2, pp.361-367, 2010. 10.1142\/s0218127410025818","DOI":"10.1142\/S0218127410025818"}],"container-title":["IEICE Transactions on Information and Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.jstage.jst.go.jp\/article\/transinf\/E100.D\/10\/E100.D_2016EDP7473\/_pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,25]],"date-time":"2025-06-25T23:37:05Z","timestamp":1750894625000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.jstage.jst.go.jp\/article\/transinf\/E100.D\/10\/E100.D_2016EDP7473\/_article"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2017]]},"references-count":29,"journal-issue":{"issue":"10","published-print":{"date-parts":[[2017]]}},"URL":"https:\/\/doi.org\/10.1587\/transinf.2016edp7473","relation":{},"ISSN":["0916-8532","1745-1361"],"issn-type":[{"value":"0916-8532","type":"print"},{"value":"1745-1361","type":"electronic"}],"subject":[],"published":{"date-parts":[[2017]]}}}