Impact of Credibility on Opinion Analysis in Social Media

• Autorzy: Ennaji F. Z. , Azaza L. , Maamar Z. , El Fazziki A. , Savonnet M. , Sadgal M. , Leclercq E. , Amarouche I. A. , Benslimane D.

Identyfikatory

DOI

10.3233/FI-2018-1725

• Konferencja: Model and Data Engineering, MEDI 2016 (6; 21-23. 09.2016; Almera, Spain)
• Języki publikacji: EN

Abstrakty

EN

In conjunction with the rapid growth and adoption of social media, people are more and more willing to share their personal experiences and opinions about products and/or services with the community. Opinions could be the basis of developing systems that would advise future users on how to proceed with any purchase without risking any disappointment. Unfortunately, opinions are not always genuine due to for instance, biased users as well as mixed feedback coming from the same users (i.e., multi-identity). This paper presents an approach for opinion analysis using credibility as a decisive criterion for supporting future users make sound decisions. The effectiveness of this approach has been tested using opinions posted on Twitter.

Słowa kluczowe

EN

credibility; multi-identity; opinion; twitter; social media

• Wydawca: IOS Press
• Czasopismo: Fundamenta Informaticae
• Rocznik: 2018
• Tom: Vol. 162, nr 4
• Strony: 259--281
• Opis fizyczny: Bibliogr. 45 poz., rys., tab., wykr.

Twórcy

autor

Ennaji F. Z.

• LISI Lab, University of Marrakech, Morocco

autor

Azaza L.

• LE2I Lab, Dijon University, France

autor

Maamar Z.

• Zayed University, Dubai, U.A.E

autor

El Fazziki A.

• LISI Lab, University of Marrakech, Morocco

autor

Savonnet M.

• LE2I Lab, Dijon University, France

autor

Sadgal M.

• LISI Lab, University of Marrakech, Morocco

autor

Leclercq E.

• LE2I Lab, Dijon University, France

autor

Amarouche I. A.

• USTHB, Algiers, Algeria

autor

Benslimane D.

• LIRIS Lab, University Claude Bernard Lyon 1, France

Bibliografia

• [1] Apache Hadoop, http://hadoop.apache.org/, Accessed: 2016-10-26.
• [2] Apache Hbase, https://hbase.apache.org/, Accessed: 2016-10-30.
• [3] Flekova L, Preotiuc-Pietro D, Ruppert E. Analysing domain suitability of a sentiment lexicon by identifying distributionally bipolar words, EMNLP, 2015. Proceedings of the 6th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis (WASSA 2015), pages 77-84. doi:0.18653/v1/W15-2911.
• [4] Bhojwani N, Shah APV. A Survey on Hadoop Hbase Ssystem, International Journal of Advance Engineering and Research Development, 2016;3(1):82-87. ISSN:2348-4470, 2348-6406.
• [5] Bravo-Marquez F, Mendoza M, Poblete B. Combining strengths, emotions and polarities for boosting Twitter sentiment analysis, Proceedings of the Second International Workshop on Issues of Sentiment Discovery and Opinion Mining, ACM, 2013. doi:10.1145/2502069.2502071.
• [6] Burnap P, Williams ML. Cyber Hate Speech on Twitter: An Application of Machine Classification and Statistical Modeling for Policy and Decision Making, Policy and Internet, 2015;7(2):223-242. URL https://doi.org/10.1002/poi3.85.
• [7] Chen HC, Goldberg M, Magdon-Ismail M. Identifying Multi-ID Users in Open Forums, Springer Berlin Heidelberg, Berlin, Heidelberg, 2004 pp. 176-186. ISBN:978-3-540-25952-7. doi:10.1007/b98042.
• [8] Day WHE, Edelsbrunner H. Efficient algorithms for agglomerative hierarchical clustering methods, Journal of Classification, 1984;1(1):7-24, ISSN:1432-1343. doi:10.1007/BF01890115.
• [9] De Marneffe MC, MacCartney B, Manning CD, et al. Generating typed dependency parses from phrase structure parses, Proceedings of LREC, 6, 2006. European Language Resources Association (ELRA). URL http://www.aclweb.org/anthology/L06-1260.
• [10] Dean J, Ghemawat S. MapReduce: Simplified Data Processing on Large Clusters, Proceedings of the 6th Conference on Symposium on Opearting Systems Design & Implementation - Volume 6, OSDI’04, USENIX Association, Berkeley, CA, USA, 2004. URL http://dl.acm.org/citation.cfm?id=1251254.1251264.
• [11] Dong XL, Berti-Equille L, Srivastava D. Integrating Conflicting Data: The Role of Source Dependence, Proc. VLDB Endow., 2009;2(1):550-561. ISSN:2150-8097. doi:10.14778/1687627.1687690.
• [12] Etuk A, Norman TJ, Ensoy M, Bisdikian C, Srivatsa M. TIDY: A trust-based approach to information fusion through diversity, Proceedings of the 16th International Conference on Information Fusion, Istanbul, July 2013 pp. 1188-1195.
• [13] Fang X, Zhan J. Sentiment analysis using product review data, Journal of Big Data, 2015;2(1):5 ISSN:2196-1115. doi:10.1186/s40537-015-0015-2.
• [14] Farrah S, El Manssouri H, Ziyati E, Ouzzif M. An Approach to Analyze Large Scale Wireless Sensors Network Data, International Research Journal of Computer Science (IRJCS), 2015;2(5):7-12. ISSN:2393-9842.
• [15] Jindal N, Liu B. Opinion Spam and Analysis, Proceedings of the 2008 International Conference on Web Search and Data Mining, WSDM ’08, ACM, New York, NY, USA, 2008 pp. 219-230. ISBN:978-1-59593-927-2. doi:10.1145/1341531.1341560.
• [16] Johansson F, Kaati L, Shrestha A. Detecting Multiple Aliases in Social Media, Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM’13, ACM, New York, NY, USA, 2013, ISBN:978-1-4503-2240-9.
• [17] Karun AK, Chitharanjan K. A review on hadoop - HDFS infrastructure extensions, 2013 IEEE Conference on Information Communication Technologies, April 2013. doi:10.1109/CICT.2013.6558077.
• [18] Kasthuri S, Jayasimman L, Jebaseeli AN. An Opinion Mining and Sentiment Analysis Techniques: A Survey, International Research Journal of Engineering and Technology (IRJET), 2016;3(2):573-575. ISSN:2395-0072, 2395-0056.
• [19] Kreowski H, Kuske S. Graph Tuple Transformation, ECEASST, 2013;62, pp. 1-24. URL http://dx.doi.org/10.14279/tuj.eceasst.62.861.855.
• [20] Ku YC, Wei CP, Hsiao HW. To whom should I listen? Finding reputable reviewers in opinion-sharing communities, Decision Support Systems, 2012;53(3):534-542. ISSN:0167-9236. URL https://doi.org/10.1016/j.dss.2012.03.003.
• [21] Li YM, Chen HM, Liou JH, Lin LF. Creating social intelligence for product portfolio design, Decision Support Systems, 2014;66:123-134. URL https://doi.org/10.1016/j.dss.2014.06.013.
• [22] Liu B. Sentiment analysis and opinion mining, Synthesis lectures on human language technologies, 2012;5(1):1-167. ISBN:1608458849 9781608458844.
• [23] Malik Z, Bouguettaya A. RATEWeb: Reputation Assessment for Trust Establishment among Web services, The VLDB Journal, 2009;18(4):885-911, ISSN:0949-877X. doi:10.1007/s00778-009-0138-1.
• [24] Mukherjee A, Kumar A, Liu B, Wang J, Hsu M, Castellanos M, Ghosh R. Spotting Opinion Spammers Using Behavioral Footprints, Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD ’13, ACM, New York, NY, USA, 2013 pp. 632-640, ISBN:978-1-4503-2174-7. doi:10.1145/2487575.2487580.
• [25] Neri F, Aliprandi C, Capeci F, Cuadros M, By T. Sentiment Analysis on Social Media, 2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, Aug 2012. ISBN:978-0-12-804412-4.
• [26] Pak A, Paroubek P. Twitter as a Corpus for Sentiment Analysis and Opinion Mining., Proceedings of the Seventh conference on International Language Resources and Evaluation (LREC’10), European Languages Resources Association (ELRA), 10, 2010 URL http://www.aclweb.org/anthology/L10-1263.
• [27] Psomakelis E, Tserpes K, Anagnostopoulos D, Varvarigou TA. Comparing methods for Twitter Sentiment Analysis, CoRR, 2015;abs/1505.02973. doi:10.5220/0005075302250232.
• [28] Qian T, Liu B. Identifying multiple userids of the same author, Association for Computational Linguistics (ACL), 2013 pp. 1124-1135. ISBN:9781937284978.
• [29] Riggs T, Wilensky R. An Algorithm for Automated Rating of Reviewers, Proceedings of the 1st ACM/IEEE-CS Joint Conference on Digital Libraries, JCDL ’01, ACM, New York, NY, USA, 2001 pp.381-387, ISBN:1-58113-345-6. doi:10.1145/379437.379731.
• [30] Sagiroglu S, Sinanc D. Big Data : A Review, 2013 International Conference on Collaboration Technologies and Systems (CTS), 2013. doi:10.1109/CTS.2013.6567202.
• [31] Saif H, Fernández M, He Y, Alani H. On stopwords, filtering and data sparsity for sentiment analysis of Twitter, LREC 2014, Ninth International Conference on Language Resources and Evaluation. Proceedings., 2014 pp. 810-817. URL http://lrec2014.lrec-conf.org/en.
• [32] Saoud Z, Faci N, Maamar Z, Benslimane D. A Fuzzy Clustering-Based Credibility Model for Trust Assessment in a Service-Oriented Architecture, 2014 IEEE 23rd International Workshops on Enabling Technologies: Infrastructures for Collaborative Enterprise (WETICE), 2014 pp. 56-61. doi:10.1109/WETICE.2014.35.
• [33] Seerat B, Azam F. Opinion mining: Issues and challenges (a survey), International Journal of Computer Applications, 2012;49(9):42-51. doi:10.5120/7658-0762.
• [34] Shaikh M, Memon N, Wiil UK. Extended approximate string matching algorithms to detect name aliases, Proceedings of 2011 IEEE International Conference on Intelligence and Security Informatics, July 2011. doi:10.1109/ISI.2011.5984085.
• [35] Sharma DM, Baig MM. Sentiment Analysis on Social Networking: A Literature Review, International Journal on IJRITCC, 2015;3(2):022-027. ISSN:2321-8169.
• [36] Sharma T, Toshniwal D. A Generalized Framework for Quantifying Trust of Social Media Text Documents, Springer International Publishing, Cham, 2016 pp. 692-713. ISBN:978-3-319-41920-6.
• [37] Shvachko K, Kuang H, Radia S, Chansler R. The Hadoop Distributed File System, 2010 IEEE 26th Symposium on Mass Storage Systems and Technologies (MSST), May 2010, doi:10.1109/MSST.2010.5496972.
• [38] Spiegel J, Polyzotis N. TuG synopses for approximate query answering, ACM Trans. Database Syst., 2009;34(1)3:1-3:56. doi:10.1145/1508857.1508860.
• [39] Taboada M, Brooke J, Tofiloski M, Voll K, Stede M. Lexicon-based methods for sentiment analysis, Computational linguistics, 2011;37(2):267-307. doi:10.1162/COLI_a_00049.
• [40] Thandar, M., Usanavasin, S.: Measuring Opinion Credibility in Twitter, Springer International Publishing, Cham, 2015 pp. 205-214. ISBN:978-3-319-19024-2.
• [41] Turney PD. Thumbs Up or Thumbs Down?: Semantic Orientation Applied to Unsupervised Classification of Reviews, Proceedings of the 40th Annual Meeting on Association for Computational Linguistics, ACL ’02, Association for Computational Linguistics, Stroudsburg, PA, USA, 2002 pp. 417-424. doi:10.3115/1073083.1073153.
• [42] Uddin MYS, Al Amin MT, Le H, Abdelzaher T, Szymanski B, Nguyen T. On diversifying source selection in social sensing, Networked Sensing Systems (INSS), 9th International Conference on Networked Sensing Systems, IEEE, 2012. doi:10.1109/INSS.2012.6240519.
• [43] Wang G, Xie S, Liu B, Yu PS. Review Graph Based Online Store Review Spammer Detection, Proceedings of the 2011 IEEE 11th International Conference on Data Mining, ICDM ’11, IEEE Computer Society, Washington, DC, USA, 2011, ISBN:978-0-7695-4408-3.
• [44] ze Wang J, Yan Z, Yang LT, Xiong Huang B. An approach to rank reviews by fusing and mining opinions based on review pertinence, Information Fusion, 23(Supplement C), 2015;23:3-15. ISSN:1566-2535. URL https://doi.org/10.1016/j.inffus.2014.04.002.
• [45] Zamani NAM, Abidin SZ, Omar N, Abiden M. Sentiment Analysis: Determining People’s Emotions in Facebook, Applied Computational Science, ISBN, 2013, pp. 978-960. ISBN:978-960-474-368-1.