Clustering and Classification of Like-Minded People from Their Tweets
Identifieur interne : 001500 ( Hal/Curation ); précédent : 001499; suivant : 001501Clustering and Classification of Like-Minded People from Their Tweets
Auteurs : Soufiene Jaffali [Tunisie] ; Salma Jamoussi [Tunisie] ; Ben Abdelmajid [Tunisie] ; Kamel Smaili [France]Source :
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Abstract
Several challenges accompanied the growth of online social networks, such as grouping people with similar interest. Grouping like-minded people is of a high importance. Indeed, it leads to many applications like link prediction and friend or product suggestion, and explains various social phenomenon. In this paper, we present two methods of grouping like-minded people based on their textual posts. Compared to three baseline methods K-Means, LDA and the Scalable Multi-stage Clustering algorithm (SMSC), our algorithms achieves relative improvements on two corpora of tweets.
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xml:lang="en">Several challenges accompanied the growth of online social networks, such as grouping people with similar interest. Grouping like-minded people is of a high importance. Indeed, it leads to many applications like link prediction and friend or product suggestion, and explains various social phenomenon. In this paper, we present two methods of grouping like-minded people based on their textual posts. Compared to three baseline methods K-Means, LDA and the Scalable Multi-stage Clustering algorithm (SMSC), our algorithms achieves relative improvements on two corpora of tweets.</div></front></TEI><hal api="V3"><titleStmt><title xml:lang="en">Clustering and Classification of Like-Minded People from Their Tweets</title><author role="aut"><persName><forename type="first">Soufiene</forename><surname>Jaffali</surname></persName><email>jaffali.soufiene@gmail.com</email><idno type="halauthor">1124579</idno><affiliation ref="#struct-206882"></affiliation></author><author role="aut"><persName><forename type="first">Salma</forename><surname>Jamoussi</surname></persName><email>jamoussi@gmail.com</email><idno type="halauthor">1124580</idno><affiliation ref="#struct-206882"></affiliation></author><author role="aut"><persName><forename type="first">Ben</forename><surname>Abdelmajid</surname></persName><email>abdelmajid.benhamadou@gmail.com</email><idno type="halauthor">1124581</idno><affiliation ref="#struct-206882"></affiliation></author><author role="aut"><persName><forename type="first">Kamel</forename><surname>Smaili</surname></persName><email>kamel.smaili@loria.fr</email><idno type="idhal">kamel-smaili</idno><idno type="halauthor">1124582</idno><affiliation ref="#struct-407170"></affiliation></author><editor role="depositor"><persName><forename>Kamel</forename><surname>Smaïli</surname></persName><email>smaili@loria.fr</email></editor></titleStmt><editionStmt><edition n="v1" type="current"><date type="whenSubmitted">2015-02-03 16:07:00</date><date type="whenWritten">2014</date><date type="whenModified">2015-09-21 11:12:52</date><date type="whenReleased">2015-02-03 16:12:40</date><date type="whenProduced">2014-12-14</date><date type="whenEndEmbargoed">2015-02-03</date><ref type="file" target="https://hal.archives-ouvertes.fr/hal-01112778/document"><date notBefore="2015-02-03"></date></ref><ref type="file" subtype="author" n="1" target="https://hal.archives-ouvertes.fr/hal-01112778/file/Soufiene.pdf"><date notBefore="2015-02-03"></date></ref></edition><respStmt><resp>contributor</resp><name key="103558"><persName><forename>Kamel</forename><surname>Smaïli</surname></persName><email>smaili@loria.fr</email></name></respStmt></editionStmt><publicationStmt><distributor>CCSD</distributor><idno type="halId">hal-01112778</idno><idno type="halUri">https://hal.archives-ouvertes.fr/hal-01112778</idno><idno type="halBibtex">jaffali:hal-01112778</idno><idno type="halRefHtml">COOL-SNA Workshop on Connecting Online and Offline Social Network Analysis' of the IEEE International Conference on Data Mining (ICDM'14), Dec 2014, Shenzen, China. 2014</idno><idno type="halRef">COOL-SNA Workshop on Connecting Online and Offline Social Network Analysis' of the IEEE International Conference on Data Mining (ICDM'14), Dec 2014, Shenzen, China. 2014</idno></publicationStmt><seriesStmt><idno type="stamp" n="CNRS">CNRS - 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Grouping like-minded people is of a high importance. Indeed, it leads to many applications like link prediction and friend or product suggestion, and explains various social phenomenon. In this paper, we present two methods of grouping like-minded people based on their textual posts. Compared to three baseline methods K-Means, LDA and the Scalable Multi-stage Clustering algorithm (SMSC), our algorithms achieves relative improvements on two corpora of tweets.</abstract></profileDesc></hal></record>Pour manipuler ce document sous Unix (Dilib)
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