Abstract
Grouping like-minded users is one of the emerging problems in Social Network Analysis. Indeed, it gives a good idea about group formation and social network evolution. Also, it explains various social phenomena and leads to many applications, such as friends suggestion and collaborative filtering. In this paper, we introduce a novel unsupervised method for grouping like-minded users within social networks. Such a method detects groups of users sharing the same interest centers and having similar opinions. In fact, the proposed method is based on extracting the interest centers and retrieving the polarities from the user’s textual posts. We validate our results by employing multiple clustering evaluation measures (recall, precision, F-score and Rand-Index). We compare our algorithm to a number of other clustering algorithms and opinion detection API. Results prove that the algorithm presented is efficient.
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References
Abrouk, L., Gross-Amblard, D., Leprovost, D.: Découverte de communautés par analyse des usages. In: EGC 2010 Workshops (Workshop Web Social), pp. A5-5–A5-16 (2010)
Abu-Jbara, A., King, B., Diab, M.T., Radev, D.R.: Identifying opinion subgroups in arabic online discussions. In: ACL (2), pp. 829–835. The Association for Computer Linguistics (2013)
Adamic, L., Glance, N.: The political blogosphere and the 2004 US election: divided they blog. In: Proceedings of the 3rd International Workshop on Link Discovery, LinkKDD 2005, pp. 36–43. ACM, New York, NY, USA (2005)
Aggarwal, C.C., Zhai, C.: A survey of text clustering algorithms. In: Aggarwal, C.C., Zhai, C. (eds.) Mining Text Data, pp. 77–128. Springer, New York (2012)
Aggarwal, C.C., Zhao, Y., Yu, P.S.: On text clustering with side information. In: 2012 IEEE 28th International Conference on Data Engineering (ICDE), pp. 894–904, April 2012
Ameur, H., Jamoussi, S.: Dynamic construction of dictionaries for sentiment classification. In: Proceedings of the 2013 IEEE International Conference on Data Mining Workshops (ICDM 2013), Dallas, Texas, USA (2013)
Angelova, R.: A neighborhood-based approach for clustering of linked document collections. In: CIKM 06: Proceedings of the 15th ACM International Conference on Information and Knowledge Management, pp. 778–779. ACM Press (2006)
Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. J. Mach. Learn. Res. 3, 993–1022 (2003)
Cattell, R.B.: The scree test for the number of factors. Multivar. Behav. Res. 1(2), 245–276 (1966)
Ding, X., Liu, B.: The utility of linguistic rules in opinion mining. In: Proceedings of the 30th Annual International ACM SIGIR conference on Research and development in information retrieval, New York, NY, USA (2007)
Esuli, A., Sebastiani, F.: SentiWordNet: a publicly available lexical resource for opinion mining. In: Proceedings of LREC 2006, the 5th Conference on Language Resources and Evaluation, Pisa, Italy (2006)
Fortunato, S.: Community detection in graphs. Phys. Rep. 486(3–5), 75–174 (2010)
Gemmell, J., Shepitsen, A., Mobasher, B., Burke, R.: Personalizing navigation in folksonomies using hierarchical tag clustering. In: Song, I.-Y., Eder, J., Nguyen, T.M. (eds.) DaWaK 2008. LNCS, vol. 5182, pp. 196–205. Springer, Heidelberg (2008)
Giles, C.L., Bollacker, K.D., Lawrence, S.: Citeseer: an automatic citation indexing system. In: International conference on digital libraries, pp. 89–98. ACM Press (1998)
Girvan, M., Newman, M.E.J.: Community structure in social and biological networks. Proc. Natl. Acad. Sci. 99(12), 7821–7826 (2002)
Généreux, M., Santini, M.: Defi: classification de textes francais subjectifs. In: Natural Language Technology Group, University of Brighton, Brighton, United Kingdom (2007)
Go, A., Bhayani, R., Huang, L.: Twitter sentiment classification using distant supervision. Processing, pp. 1–6 (2009)
Handcock, M.S., Raftery, A.E, Tantrum, J.M.: Model-based clustering for social networks (2007)
Hang, C., Vibhu, O.M., Mayur, D.: Comparative experiments on sentiment classification for online product reviews. In: AAAI, Boston, Massachusetts (2006)
Hannachi, L., Asfari, O., Benblidia, N., Bentayeb, F., Kabachi, N., Boussaid, O.: Community extraction based on topic-driven-model for clustering users tweets. In: Zhou, S., Zhang, S., Karypis, G. (eds.) ADMA 2012. LNCS, vol. 7713, pp. 39–51. Springer, Heidelberg (2012)
Harb, A., Plantié, M., Dray, G., Roche, M., Trousset, F., Poncelet, P.: Web opinion mining: how to extract opinions from blogs? In: Proceedings of the 5th International Conference on Soft Computing as Transdisciplinary Science and Technology, CSTST 2008, pp. 211–217. ACM, New York, NY, USA (2008)
Hassan, A., Abu-Jbara, A., Radev, D.R.: Detecting subgroups in online discussions by modeling positive and negative relations among participants. In: EMNLP-CoNLL, pp. 59–70. ACL (2012)
Hogenboom, A., Bal, D., Frasincar, F., Bal, M., de Jong, F., Kaymak, U.: Exploiting emoticons in sentiment analysis. In: Proceedings of the 28th Annual ACM Symposium on Applied Computing, SAC 2013, pp. 703–710. ACM, New York, NY, USA (2013)
Jaffali, S., Jamoussi, S.: Principal component analysis neural network for textual document categorization and dimension reduction. In: 2012 6th International Conference on Sciences of Electronics, Technologies of Information and Telecommunications (SETIT), pp. 835–839 (2012)
Kaiser, H.F.: The application of electronic computers to factor analysis. Educ. Psychol. Measur. 20(1), 141–151 (1960)
Li, X., Guo, L., Zhao, Y.E.: Tag-based social interest discovery. In: Proceedings of the 17th International Conference on World Wide Web, WWW 2008, pp. 675–684. ACM, New York, NY, USA (2008)
Liang, H., Xu, Y., Li, Y.: Mining users’ opinions based on item folksonomy and taxonomy for personalized recommender systems. In: Fan, W., Hsu, W., Webb, G.I., Liu, B., Zhang, C., Gunopulos, D. Wu, X. (eds.) ICDM Workshops, pp. 1128–1135. IEEE Computer Society (2010)
Lin, Y., Sundaram, H., Chi, Y., Tatemura, J., Tseng, B.: Discovery of blog communities based on mutual awareness. In: Proceedings of the 3rd Annual Workshop on the Weblogging Ecosystem (2006)
Liu, J.: Comparative analysis for k-means algorithms in network community detection. In: Cai, Z., Hu, C., Kang, Z., Liu, Y. (eds.) ISICA 2010. LNCS, vol. 6382, pp. 158–169. Springer, Heidelberg (2010)
MacQueen, J.B.: Some methods for classification and analysis of multivariate observations. In: Le Cam, L. M., Neyman, J. (eds.) Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability, vol. 1, pp. 281–297. University of California Press (1967)
Mahajan, M., Nimbhorkar, P., Varadarajan, K.: The planar k-means problem is NP-hard. Theor. Comput. Sci. 442, 13–21 (2012)
McCallum, A., Wang, X., Corrada-Emmanuel, A.: Topic and role discovery in social networks with experiments on enron and academic email. J. Artif. Int. Res. 30(1), 249–272 (2007)
McGlohon, M., Akoglu, L., Faloutsos, C.: Statistical properties of social networks. In: Aggarwal, C.C. (ed.) Social Network Data Analytics, pp. 17–42. Springer, New York (2011)
Newman, M.E.J., Girvan, M.: Finding and evaluating community structure in networks. Phys. Rev. E 69, 026113 (2004)
Nigam, K., Hurst, M.: Towards a robust metric of polarity. In: James, G.S., Qu, Y., Wiebe, J. (eds.) Computing Attitude and Affect in Text: Theories and Applications. The Information Retrieval Series, vol. 20. Springer, Dordrecht (2006)
Ounis, I., Lin, J. Soboroff, I.: Overview of the trec-2011 microblog track. In: TREC (2011)
Pak, A., Paroubek, P.: Construction dun lexique affectif pour le franais partir de twitter. Universit de Paris-Sud, Cedex, France, TALN 2010, Juillet 2010
Palsetia, D., Patwary, M.M., Zhang, K., Lee, K., Moran, C., Xie, Y., Honbo, D., Agrawal, A., Liao, W., Choudhary, A.: User-interest based community extraction in social networks. In: The 6th SNA-KDD Workshop 12. ACM (2012)
Pearson, K.: On lines and planes of closest fit to points in space. Philos. Mag. 2, 559–572 (1901)
Poirier, D.: Des textes communautaires à la recommandation. Ph.D. thesis, Université d’Orléans (2011)
Qi, G.-J., Aggarwal, C.C., Huang, T.: Community detection with edge content in social media networks. In: 2012 IEEE 28th International Conference on Data Engineering (ICDE), pp. 534–545, April 2012
Raîche, G., Walls, T.A., Magis, D., Riopel, M., Blais, J.: Non-graphical solutions for cattells scree test. Methodol. Eur. J. Res. Methods Behav. Soc. Sci. 9(1), 23–29 (2013)
Rand, W.M.: Objective criteria for the evaluation of clustering methods. J. Am. Stat. Assoc. 66(336), 846–850 (1971)
Van Rijsbergen, C.J.: Information Retrieval, 2nd edn. Butterworth-Heinemann, Newton (1979)
Sachan, M., Contractor, D., Faruquie, T. A., Subramaniam, L.V.: Using content and interactions for discovering communities in social networks. In: Proceedings of the 21st International Conference on World Wide Web, WWW 2012, pp. 331–340. ACM, New York, NY, USA (2012)
Sanders, N.J.: Sanders-Twitter sentiment corpus. Sanders Analytics LLC, October 2011
Solakidis, G.S., Vavliakis, K.N., Mitkas, P.A.: Multilingual sentiment analysis using emoticons and keywords. In: 2014 IEEE/WIC/ACM International Joint Conferences on Web Intelligence (WI) and Intelligent Agent Technologies (IAT), Warsaw, Poland, 11–14 August 2014 , vol. I, pp. 102–109 (2014)
Tang, L., Wang, X., Liu, H.: Community detection via heterogeneous interaction analysis. Data Min. Knowl. Discov. 25(1), 1–33 (2012)
Tsur, O., Littman, A., Rappoport, A.: Efficient clustering of short messages into general domains. In: Kiciman, E., Ellison, N.B., Hogan, B., Resnick, P., Soboroff, I. (eds.) ICWSM. The AAAI Press (2013)
Turney, M., Peter, D., Littman, L.: Measuring praise and criticism: inference of semantic orientation from association. ACM Trans. Inf. Syst. (TOIS) 21(4), 315–346 (2003)
Turney, P.: Thumbs up or thumbs down? semantic orientation applied to unsupervised classification of reviews. In: Proceedings of the Association for Computational Linguistics (ACL), Philadelphia, Pennsylvania (2002)
Vincze, N., Bestgen, Y.: Identification de mots germes pour la construction d’un lexique de valence au moyen d’une procédure supervisée. In: Actes de la 18e conférence sur le Traitement Automatique des Langues Naturelles, TALN 2011, Montpellier, France (2011)
Wang, X., Liu, H., Fan, W.: Connecting users with similar interests via tag network inference. In: The 20th ACM Conference on Information and Knowledge Management (CIKM), Glasgow, Scotland, UK (2011)
Wilson, T., Wiebe, J., Hwa, R.: Just how mad are you? finding strong and weak opinion clauses. In: Proceedings of AAAI, San Jose, US (2004)
Wilson, T., Wiebe, J., Hoffmann, P.: Recognizing contextual polarity in phrase-level sentiment analysis. In: Proceedings of the Human Language Technology Conference and the Conference on Empirical Methods in Natural Language Processing (HLT/EMNLP), Vancouver, CA (2005)
Yang, T., Jin, R., Chi, Y., Zhu, S.: Combining link and content for community detection: a discriminative approach. In: Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2009, pp. 927–936. ACM, New York, NY, USA (2009)
Yessenov, K.: Sentiment analysis of movie review comments (2009)
Zhou, D., Resnick, P., Mei, Q.: Classifying the political leaning of news articles and users from user votes. In: Adamic, L.A., Baeza-Yates, R.A., Counts, S. (eds.) ICWSM. The AAAI Press (2011)
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Jaffali, S., Ameur, H., Jamoussi, S., Hamadou, A.B. (2015). GLIO: A New Method for Grouping Like-Minded Users. In: Nguyen, N. (eds) Transactions on Computational Collective Intelligence XVIII. Lecture Notes in Computer Science(), vol 9240. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-48145-5_3
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