{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,4]],"date-time":"2026-05-04T10:20:28Z","timestamp":1777890028490,"version":"3.51.4"},"reference-count":32,"publisher":"SAGE Publications","issue":"1","license":[{"start":{"date-parts":[[2017,2,21]],"date-time":"2017-02-21T00:00:00Z","timestamp":1487635200000},"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":["Web Intelligence"],"published-print":{"date-parts":[[2017,2,21]]},"abstract":"<jats:p>Many people share their daily events and opinions on Twitter. Some tweets are beneficial and others are related to such aspects of a user\u2019s real-life as eating, traffic conditions, and weather. In this paper, we propose an inference method of the real-life aspect distribution of tweets using labeled tweets. Our method infers the aspect probability distributions by a hierarchical estimation framework (HEF), which is hierarchically composed of both unsupervised and supervised machine learning methods. In the first phase, it extracts topics from a sea of tweets using Latent Dirichlet Allocation (LDA). In the second phase, it builds associations between topics and real-life aspects using a small set of labeled tweets. The probability distribution of aspects is inferred using the associations based on the bag of terms extracted from unknown tweets. Our sophisticated experimental evaluations with a large amount of actual tweets demonstrate the high efficiency and robustness of our inference method. Especially in the case of single-label training, HEF showed significantly lower JSD values than other baseline methods, such as Naive Bayes, SVM, and L-LDA.<\/jats:p>","DOI":"10.3233\/web-170352","type":"journal-article","created":{"date-parts":[[2017,2,21]],"date-time":"2017-02-21T11:38:21Z","timestamp":1487677101000},"page":"55-65","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":0,"title":["Life aspect inference of tweets based on probability distribution"],"prefix":"10.1177","volume":"15","author":[{"given":"Shuhei","family":"Yamamoto","sequence":"first","affiliation":[{"name":"Faculty of Library, Information and Media Science, University of Tsukuba, Japan. E-mails:\u00a0,\u00a0"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Noriko","family":"Kando","sequence":"additional","affiliation":[{"name":"Information and Society Research Division, National Institute of Informatics, Japan. E-mail:\u00a0"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tetsuji","family":"Satoh","sequence":"additional","affiliation":[{"name":"Faculty of Library, Information and Media Science, University of Tsukuba, Japan. E-mails:\u00a0,\u00a0"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","published-online":{"date-parts":[[2025,2,3]]},"reference":[{"key":"ref001","unstructured":"E.\u00a0Aramaki, S.\u00a0Maskawa and M.\u00a0Morita, Twitter catches the flu: Detecting influenza epidemics using Twitter, in: Proceedings of the EMNLP 2011, AAAI, 2011, pp.\u00a01568\u20131576."},{"key":"ref002","first-page":"993","volume":"3","author":"Blei D.M.","year":"2003","journal-title":"JMLR"},{"key":"ref003","doi-asserted-by":"publisher","DOI":"10.1145\/1961189.1961199"},{"issue":"1","key":"ref004","first-page":"37","volume":"20","author":"Cohen J.","year":"1960","journal-title":"EPM"},{"issue":"3","key":"ref005","first-page":"273","volume":"20","author":"Cortes C.","year":"1995","journal-title":"JMLR"},{"key":"ref006","unstructured":"Q.\u00a0Diao, J.\u00a0Jiang, F.\u00a0Zhu and E.P.\u00a0Lim, Finding bursty topics from microblogs, in: Proceedings of the ACL 2012, ACM, 2012, pp.\u00a0536\u2013544."},{"issue":"2","key":"ref007","first-page":"103","volume":"29","author":"Domingos P.","year":"1997","journal-title":"JMLR"},{"key":"ref008","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.0307752101"},{"key":"ref009","doi-asserted-by":"crossref","unstructured":"A.\u00a0Ishino, H.\u00a0Nanba and T.\u00a0Takezawa, Automatic compilation of an online travel portal from automatically extracted travel blog entries, in: Information and Communication Technologies in Tourism 2011, Springer, 2011, pp.\u00a0113\u2013124. doi:10.1007\/978-3-7091-0503-0_10.","DOI":"10.1007\/978-3-7091-0503-0_10"},{"key":"ref010","unstructured":"Y.\u00a0Kase and T.\u00a0Miura, Mining classes by multi-label classification, in: Proceedings of the EGC 2015, RNTI, 2015, pp.\u00a077\u201382."},{"key":"ref011","doi-asserted-by":"crossref","unstructured":"Z.\u00a0Ma, A.\u00a0Sun, Q.\u00a0Yuan and G.\u00a0Cong, Tagging your tweets: A probabilistic modeling of hashtag annotation in Twitter, in: Proceedings of the CIKM 2014, ACM, 2014, pp.\u00a0999\u20131008.","DOI":"10.1145\/2661829.2661903"},{"key":"ref012","unstructured":"J.B.H.\u00a0Mao and A.\u00a0Pepe, Modeling public mood and emotion: Twitter sentiment and socio-economic phenomena, in: Proceedings of the ICWSM 2011, AAAI, 2011, pp.\u00a0450\u2013453."},{"key":"ref013","doi-asserted-by":"crossref","unstructured":"M.\u00a0Mathioudakis and N.\u00a0Koudas, Twittermonitor: Trend detection over the Twitter stream, in: Proceedings of the SIGMOD 2010, ACM, 2010, pp.\u00a01155\u20131158.","DOI":"10.1145\/1807167.1807306"},{"key":"ref014","unstructured":"Y.\u00a0Mizunuma, S.\u00a0Yamamoto, Y.\u00a0Yamaguchi, A.\u00a0Ikeuchi, T.\u00a0Satoh and S.\u00a0Shimada, Twitter bursts: Analysis of their occurrences and classifications, in: Proceedings of the ICDS 2014, IARIA XPS, 2014, pp.\u00a0182\u2013187."},{"key":"ref015","unstructured":"K.P.\u00a0Murphy, Machine Learning: A Probabilistic Perspective, The MIT Press, 2012, p.\u00a058."},{"key":"ref016","doi-asserted-by":"crossref","unstructured":"A.\u00a0Rajadesingan, R.\u00a0Zafarani and H.\u00a0Liu, Sarcasm detection on Twitter: A behavioral modeling approach, in: Proceedings of the WSDM 2015, ACM, 2015, pp.\u00a097\u2013106.","DOI":"10.1145\/2684822.2685316"},{"key":"ref017","doi-asserted-by":"crossref","unstructured":"D.\u00a0Ramage, D.\u00a0Hall, R.\u00a0Nallapati and C.D.\u00a0Manning, Labeled LDA: A supervised topic model for credit attribution in multi-labeled corpora, in: Proceedings of the EMNLP 2009, ACM, 2009, pp.\u00a0248\u2013256.","DOI":"10.3115\/1699510.1699543"},{"key":"ref018","unstructured":"M.\u00a0Riedl and C.\u00a0Biemann, TopicTiling: A text segmentation algorithm based on LDA, in: Proceedings of the ACL 2012, ACM, 2012, pp.\u00a037\u201342."},{"key":"ref019","doi-asserted-by":"crossref","unstructured":"T.\u00a0Sakaki, M.\u00a0Okazaki and Y.\u00a0Matsuo, Earthquake shakes Twitter users: Real-time event detection by social sensors, in: Proceedings of the WWW 2010, ACM, 2010, pp.\u00a0851\u2013860.","DOI":"10.1145\/1772690.1772777"},{"key":"ref020","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2012.29"},{"key":"ref021","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-24800-9_34"},{"key":"ref022","unstructured":"Twitter, Twitter reports fourth quarter and fiscal year 2013 results, 2014, available at https:\/\/investor.twitterinc.com\/releasedetail.cfm?ReleaseID=823321."},{"key":"ref023","doi-asserted-by":"crossref","unstructured":"E.\u00a0Voorhees and D.\u00a0Tice, The TREC-8 question answering track evaluation, in: Proceedings of the TREC-8, ACM, 1999, pp.\u00a077\u201382.","DOI":"10.6028\/NIST.SP.500-246.qa-overview"},{"key":"ref024","doi-asserted-by":"crossref","unstructured":"B.\u00a0Wang, C.\u00a0Wang, J.\u00a0Bu, C.\u00a0Chen, W.V.\u00a0Zhang, D.\u00a0Cai and X.\u00a0He, Whom to mention: Expand the diffusion of tweets by @ recommendation on micro-blogging systems, in: Proceedings of the WWW 2013, ACM, 2013, pp.\u00a01331\u20131340.","DOI":"10.1145\/2488388.2488505"},{"issue":"2","key":"ref025","first-page":"173","volume":"3","author":"Wei Z.","year":"2011","journal-title":"International Journal of Advanced Intelligence"},{"key":"ref026","doi-asserted-by":"publisher","DOI":"10.2307\/2332510"},{"key":"ref027","first-page":"975","volume":"5","author":"Wu T.-F.","year":"2004","journal-title":"JMLR"},{"issue":"11","key":"ref028","first-page":"1184","volume":"53","author":"Yamamoto M.","year":"2012","journal-title":"IPSJ Magazine"},{"key":"ref029","doi-asserted-by":"crossref","unstructured":"S.\u00a0Yamamoto and T.\u00a0Satoh, Hierarchical estimation framework of multi-label classifying: A case of tweets classifying into real life aspects, in: Proceedings of the ICWSM 2015, ACM, 2015, pp.\u00a0523\u2013532.","DOI":"10.1609\/icwsm.v9i1.14592"},{"key":"ref030","doi-asserted-by":"crossref","unstructured":"Y.C.\u00a0Zhang, D.O.\u00a0S\u00e9aghdha, D.\u00a0Quercia and T.\u00a0Jambor, Auralist: Introducing serendipity into music recommendation, in: Proceedings of the WSDM 2012, ACM, 2012, pp.\u00a013\u201322.","DOI":"10.1145\/2124295.2124300"},{"key":"ref031","unstructured":"W.X.\u00a0Zhao, J.\u00a0Jiang, J.\u00a0He, Y.\u00a0Song, P.\u00a0Achananuparp, E.P.\u00a0Lim and X.\u00a0Li, Topical keyphrase extraction from Twitter, in: Proceedings of the HLT 2011, ACM, 2011, pp.\u00a0379\u2013388."},{"key":"ref032","doi-asserted-by":"crossref","unstructured":"Z.\u00a0Zhao and Q.\u00a0Mei, Questions about questions: An empirical analysis of information needs on Twitter, in: Proceedings of the WWW 2013, ACM, 2013, pp.\u00a01545\u20131556.","DOI":"10.1145\/2488388.2488523"}],"container-title":["Web Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.3233\/WEB-170352","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/full-xml\/10.3233\/WEB-170352","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.3233\/WEB-170352","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T05:26:59Z","timestamp":1777613219000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.sagepub.com\/doi\/full\/10.3233\/WEB-170352"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2017,2,21]]},"references-count":32,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2017,2,21]]}},"alternative-id":["10.3233\/WEB-170352"],"URL":"https:\/\/doi.org\/10.3233\/web-170352","relation":{},"ISSN":["2405-6456","2405-6464"],"issn-type":[{"value":"2405-6456","type":"print"},{"value":"2405-6464","type":"electronic"}],"subject":[],"published":{"date-parts":[[2017,2,21]]}}}