{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,7,11]],"date-time":"2024-07-11T05:54:57Z","timestamp":1720677297231},"reference-count":31,"publisher":"Institute of Electronics, Information and Communications Engineers (IEICE)","issue":"11","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEICE Trans. Inf. &amp; Syst."],"published-print":{"date-parts":[[2019,11,1]]},"DOI":"10.1587\/transinf.2018edp7243","type":"journal-article","created":{"date-parts":[[2019,10,31]],"date-time":"2019-10-31T22:49:09Z","timestamp":1572562149000},"page":"2148-2158","source":"Crossref","is-referenced-by-count":3,"title":["Improved LDA Model for Credibility Evaluation of Online Product Reviews"],"prefix":"10.1587","volume":"E102.D","author":[{"given":"Xuan","family":"WANG","sequence":"first","affiliation":[{"name":"School of Computer Engineering and Science, Shanghai University"}]},{"given":"Bofeng","family":"ZHANG","sequence":"additional","affiliation":[{"name":"School of Computer Engineering and Science, Shanghai University"}]},{"given":"Mingqing","family":"HUANG","sequence":"additional","affiliation":[{"name":"School of Computer Engineering and Science, Shanghai University"}]},{"given":"Furong","family":"CHANG","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Kashgar University"}]},{"given":"Zhuocheng","family":"ZHOU","sequence":"additional","affiliation":[{"name":"School of Computer Engineering and Science, Shanghai University"}]}],"member":"532","reference":[{"key":"1","doi-asserted-by":"publisher","unstructured":"[1] M.J. Metzger, A.J. Flanagin, K. Eyal, D.R. Lemus, and R.M. Mccann, \u201cCredibility for the 21st century: Integrating perspectives on source, message, and media credibility in the contemporary media environment,\u201d Annals of the International Communication Association, vol.27, no.1, pp.293-335, 2003. 10.1080\/23808985.2003.11679029","DOI":"10.1080\/23808985.2003.11679029"},{"key":"2","doi-asserted-by":"publisher","unstructured":"[2] T.L. Ngo-Ye and A.P. Sinha, \u201cThe influence of reviewer engagement characteristics on online review helpfulness: A text regression model,\u201d Decision Support Systems, vol.61, no.4, pp.47-58, May 2014. 10.1016\/j.dss.2014.01.011","DOI":"10.1016\/j.dss.2014.01.011"},{"key":"3","doi-asserted-by":"crossref","unstructured":"[3] F. Yang, Y. Liu, X. Yu, and M. Yang, \u201cAutomatic detection of rumor on sina weibo,\u201d pp.1-7, 2012. 10.1145\/2350190.2350203","DOI":"10.1145\/2350190.2350203"},{"key":"4","unstructured":"[4] Z. Jin, J. Cao, Y. Zhang, and J. Luo, \u201cNews verification by exploiting conflicting social viewpoints in microblogs,\u201d AAAI, pp.2972-2978, 2016."},{"key":"5","doi-asserted-by":"publisher","unstructured":"[5] C.C. Chen and Y.D. Tseng, \u201cQuality evaluation of product reviews using an information quality framework,\u201d Decision Support Systems, vol.50, no.4, pp.755-768, March 2011. 10.1016\/j.dss.2010.08.023","DOI":"10.1016\/j.dss.2010.08.023"},{"key":"6","doi-asserted-by":"publisher","unstructured":"[6] M.J. Metzger, A.J. Flanagin, and R.B. Medders, \u201cSocial and heuristic approaches to credibility evaluation online,\u201d J. communication, vol.60, no.3, pp.413-439, 2010. 10.1111\/j.1460-2466.2010.01488.x","DOI":"10.1111\/j.1460-2466.2010.01488.x"},{"key":"7","doi-asserted-by":"crossref","unstructured":"[7] J. Aigner, A. Durchardt, T. Kersting, M. Kattenbeck, and D. Elsweiler, \u201cManipulating the perception of credibility in refugee related social media posts,\u201d Proc. 2017 Conference on Conference Human Information Interaction and Retrieval, pp.297-300, ACM, 2017. 10.1145\/3020165.3022137","DOI":"10.1145\/3020165.3022137"},{"key":"8","doi-asserted-by":"crossref","unstructured":"[8] M. Alrubaian, M. Al-Qurishi, M. Al-Rakhami, and A. Alamri, \u201cA credibility assessment model for online social network content,\u201d in From Social Data Mining and Analysis to Prediction and Community Detection, pp.61-77, Springer, 2017. 10.1007\/978-3-319-51367-6_3","DOI":"10.1007\/978-3-319-51367-6_3"},{"key":"9","doi-asserted-by":"crossref","unstructured":"[9] J. Ito, J. Song, H. Toda, Y. Koike, and S. Oyama, \u201cAssessment of tweet credibility with lda features,\u201d pp.953-958, 2015. 10.1145\/2740908.2742569","DOI":"10.1145\/2740908.2742569"},{"key":"10","doi-asserted-by":"crossref","unstructured":"[10] S. Deerwester, S.T. Dumais, G.W. Furnas, T.K. Landauer, and R. Harshman, \u201cIndexing by latent semantic analysis,\u201d J. American society for information science, vol.41, no.6, p.391, 1990. 10.1002\/(SICI)1097-4571(199009)41:6%3C391::AID-ASI1%3E3.0.CO;2-9","DOI":"10.1002\/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9"},{"key":"11","doi-asserted-by":"crossref","unstructured":"[11] T. Hofmann, \u201cProbabilistic latent semantic analysis,\u201d Proc. 15th Conference on Uncertainty in Artificial Intelligence, pp.289-296, 1999.","DOI":"10.1145\/312624.312649"},{"key":"12","unstructured":"[12] D.M. Blei, A.Y. Ng, and M.I. Jordan, \u201cLatent Dirichlet allocation,\u201d J. Machine Learning Research, vol.3, no.Jan, pp.993-1022, 2003."},{"key":"13","unstructured":"[13] D. Ramage, D. Hall, R. Nallapati, and C.D. Manning, \u201cLabeled lda: A supervised topic model for credit attribution in multi-labeled corpora,\u201d Proc. 2009 Conference on Empirical Methods in Natural Language Processing: Volume 1-Volume 1, pp.248-256, Association for Computational Linguistics, 2009. 10.3115\/1699510.1699543"},{"key":"14","doi-asserted-by":"crossref","unstructured":"[14] I. Titov and R. McDonald, \u201cModeling online reviews with multi-grain topic models,\u201d Proc. 17th Int. Conf. World Wide Web, pp.111-120, ACM, 2008. 10.1145\/1367497.1367513","DOI":"10.1145\/1367497.1367513"},{"key":"15","doi-asserted-by":"crossref","unstructured":"[15] H. Chen, H. Yin, X. Li, M. Wang, W. Chen, and T. Chen, \u201cPeople opinion topic model: opinion based user clustering in social networks,\u201d Proc. 26th Int. Conf. World Wide Web Companion, pp.1353-1359, International World Wide Web Conferences Steering Committee, 2017. 10.1145\/3041021.3051159","DOI":"10.1145\/3041021.3051159"},{"key":"16","unstructured":"[16] T. Iwata, S. Watanabe, T. Yamada, and N. Ueda, \u201cTopic tracking model for analyzing consumer purchase behavior,\u201d IJCAI, pp.1427-1432, 2009."},{"key":"17","doi-asserted-by":"crossref","unstructured":"[17] T. Kurashima, T. Iwata, T. Hoshide, N. Takaya, and K. Fujimura, \u201cGeo topic model: joint modeling of user&apos;s activity area and interests for location recommendation,\u201d Proc. sixth ACM International Conference on Web search and data mining, pp.375-384, ACM, 2013. 10.1145\/2433396.2433444","DOI":"10.1145\/2433396.2433444"},{"key":"18","doi-asserted-by":"crossref","unstructured":"[18] C. Chemudugunta, P. Smyth, and M. Steyvers, \u201cModeling general and specific aspects of documents with a probabilistic topic model,\u201d Advances in neural information processing systems, pp.241-248, 2007. 10.7551\/mitpress\/7503.003.0035","DOI":"10.7551\/mitpress\/7503.003.0035"},{"key":"19","doi-asserted-by":"crossref","unstructured":"[19] C. Lin and Y. He, \u201cJoint sentiment\/topic model for sentiment analysis,\u201d Proc. 18th ACM conference on Information and knowledge management, pp.375-384, ACM, 2009. 10.1145\/1645953.1646003","DOI":"10.1145\/1645953.1646003"},{"key":"20","doi-asserted-by":"crossref","unstructured":"[20] S. Wang, Z. Chen, and B. Liu, \u201cMining aspect-specific opinion using a holistic lifelong topic model,\u201d Proc. 25th Int. Conf. world wide web, pp.167-176, International World Wide Web Conferences Steering Committee, 2016. 10.1145\/2872427.2883086","DOI":"10.1145\/2872427.2883086"},{"key":"21","unstructured":"[21] Y.W. Teh, M.I. Jordan, M.J. Beal, and D.M. Blei, \u201cSharing clusters among related groups: Hierarchical Dirichlet processes,\u201d Advances in Neural Information Processing Systems, pp.1385-1392, 2005."},{"key":"22","unstructured":"[22] T.L. Griffiths, M.I. Jordan, J.B. Tenenbaum, and D.M. Blei, \u201cHierarchical topic models and the nested Chinese restaurant process,\u201d Advances in Neural Information Processing Systems, pp.17-24, 2004."},{"key":"23","doi-asserted-by":"crossref","unstructured":"[23] X.H. Phan, L.M. Nguyen, and S. Horiguchi, \u201cLearning to classify short and sparse text &amp; web with hidden topics from large-scale data collections,\u201d Proc. 17th Int. Conf. World Wide Web, pp.91-100, ACM, 2008. 10.1145\/1367497.1367510","DOI":"10.1145\/1367497.1367510"},{"key":"24","doi-asserted-by":"crossref","unstructured":"[24] P. Wang, H. Zhang, Y.F. Wu, B. Xu, and H.W. Hao, \u201cA robust framework for short text categorization based on topic model and integrated classifier,\u201d 2014 International Joint Conference on Neural Networks (IJCNN), pp.3534-3539, IEEE, 2014. 10.1109\/IJCNN.2014.6889589","DOI":"10.1109\/IJCNN.2014.6889589"},{"key":"25","doi-asserted-by":"publisher","unstructured":"[25] Y. Zhu, L. Li, and L. Luo, \u201cLearning to classify short text with topic model and external knowledge,\u201d Int. Conf. Knowledge Science, Engineering and Management, pp.493-503, Springer, 2013. 10.1007\/978-3-642-39787-5_41","DOI":"10.1007\/978-3-642-39787-5_41"},{"key":"26","doi-asserted-by":"crossref","unstructured":"[26] L. He, Y. Du, and Y. Ye, \u201cTracking topic trends for short texts,\u201d China Conference on Knowledge Graph and Semantic Computing, pp.117-128, Springer, 2017. 10.1007\/978-981-10-7359-5_12","DOI":"10.1007\/978-981-10-7359-5_12"},{"key":"27","doi-asserted-by":"publisher","unstructured":"[27] Y. Zuo, J. Zhao, and K. Xu, \u201cWord network topic model: a simple but general solution for short and imbalanced texts,\u201d Knowledge and Information Systems, vol.48, no.2, pp.379-398, Aug. 2016. 10.1007\/s10115-015-0882-z","DOI":"10.1007\/s10115-015-0882-z"},{"key":"28","doi-asserted-by":"publisher","unstructured":"[28] X. Cheng, X. Yan, Y. Lan, and J. Guo, \u201cBtm: Topic modeling over short texts,\u201d IEEE Trans. Knowl. Data Eng., vol.26, no.12, pp.2928-2941, Dec. 2014. 10.1109\/TKDE.2014.2313872","DOI":"10.1109\/TKDE.2014.2313872"},{"key":"29","doi-asserted-by":"crossref","unstructured":"[29] E. Momeni, K. Tao, B. Haslhofer, and G.J. Houben, \u201cIdentification of useful user comments in social media: a case study on flickr commons,\u201d Proc. 13th ACM\/IEEE-CS joint conference on Digital libraries, pp.1-10, ACM, 2013. 10.1145\/2467696.2467711","DOI":"10.1145\/2467696.2467711"},{"key":"30","doi-asserted-by":"crossref","unstructured":"[30] J.J. McAuley and J. Leskovec, \u201cFrom amateurs to connoisseurs: modeling the evolution of user expertise through online reviews,\u201d Proc. 22nd Int. Conf. World Wide Web, pp.897-908, ACM, 2013. 10.1145\/2488388.2488466","DOI":"10.1145\/2488388.2488466"},{"key":"31","doi-asserted-by":"crossref","unstructured":"[31] O. Tsur and A. Rappoport, \u201cRevrank: A fully unsupervised algorithm for selecting the most helpful book reviews,\u201d ICWSM, 2009.","DOI":"10.1609\/icwsm.v3i1.13945"}],"container-title":["IEICE Transactions on Information and Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.jstage.jst.go.jp\/article\/transinf\/E102.D\/11\/E102.D_2018EDP7243\/_pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,9,22]],"date-time":"2023-09-22T10:21:06Z","timestamp":1695378066000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.jstage.jst.go.jp\/article\/transinf\/E102.D\/11\/E102.D_2018EDP7243\/_article"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,11,1]]},"references-count":31,"journal-issue":{"issue":"11","published-print":{"date-parts":[[2019]]}},"URL":"https:\/\/doi.org\/10.1587\/transinf.2018edp7243","relation":{},"ISSN":["0916-8532","1745-1361"],"issn-type":[{"value":"0916-8532","type":"print"},{"value":"1745-1361","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,11,1]]}}}