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LEVERAGING THE MACHINE LEARNING ALGORITHMS AND TOOLS TO ENHANCE BUSINESS INTELLIGENCE LINKED TO SENTIMENT ANALYSIS

Shweta

73-81

Vol. 9, Jan-Jun, 2019

Date of Submission: 2019-02-17 Date of Acceptance: 2019-04-23 Date of Publication: 2019-05-03

Abstract

This paper proposes the utilization of sentiment analysis classification as a valuable approach for analyzing textual data sourced from various internet resources. Sentiment analysis, a data mining method employing machine learning techniques, offers insights into the opinions, reviews, feedback, and suggestions available online. Given the vast array of user opinions, it is imperative to uncover, analyze, and synthesize these viewpoints for informed decision-making. Sentiment analysis provides real-time, efficient feedback from consumers, significantly impacting the decision-making process in the business domain. Over the past decade, there has been a notable increase in research activity and emphasis on exploratory research methodologies. However, we observe certain gaps in Business Intelligence research methodologies, as well as identify areas that warrant further investigation.

References

  1. L. Issacs, “Rolling the Dice with Predictive Coding: Leveraging AnalyticsTechnology for Information Governance”, Information Management(47:1), 2013, pp. 22- 26.
  2. P. Malik, “Governing Big Data: Principles and Practices”, IBM Journal ofResearch and Development (57 :¾), 2013, pp. 1-13.
  3. R.M. Hogarth, E. Soyer, “Using Simulated Experience to Make Sense ofBig Data”, MIT Sloan Management Review (spring), 2015, pp. 5-10.
  4. A.Gandomi, and M. Haider, “Beyond the Hype: Big Data Concepts,Methods, and Analytics”, International Journal of InformationManagement (35), 2015, pp. 137-144.
  5. Vedder, R. G., Vanecek, M. T., Guynes, C. S., & Cappel, J. J. (1999).CEO and CIO Perspectives on Competitive Intelligence.Communications of the ACM, 42(8), 108–116.
  6. Liu, Bing. (2010). Sentiment analysis and subjectivity. Handbook ofNatural Language Processing, 2nd ed. Chapman and Hall: Florida.
  7. Kumar, Akshi, and Sebastian, Teja, Mary. (2012). “Sentiment analysis. Aperspective on its past present and future.” International Journal ofIntelligent Systems and Applications, 4 (10): 1-14.
  8. B. Pang, L. Lee, and S. Vaithyanathan,“Thumbs up?:Sentimentclassification using machine learning techniques,”inProc.ACL-02Conf.Empirical Methods Natural Lang. Process., 2002, pp. 79–86.
  9. K. Dave, S. Lawrence, and D. M. Pennock, “Mining the peanut gallery:opinion extraction and semantic classification of product reviews,” inProc. 12th Int. Conf. World Wide Web, New York: ACM, 2003, pp.519–528.
  10. V.Hatzivassiloglou and K. R. McKeown, “Predicting the semanticorientation of adjectives,” in Proc. 8th Conf. Eur. Chap. Assoc.Comput.Linguist., Morristown, NJ: Assoc. Comput. Linguist, 1997,pp.174–181
  11. A. Esuli and F. Sebastiani, “Determining the semantic orientation ofterms through gloss classification,” in Proc. 14th ACM Int. Conf. Inf.Knowl.Manage., 2005, pp. 617–624.
  12. A. Esuli and F. Sebastiani, “SENTIWORDNET: A publicly availablelexical resource for opinion mining,” in Proc. 5th Conf. Lang. Res.Eval., 2006, pp. 417–422
  13. International Journal of Ad hoc, Sensor & Ubiquitous Computing(IJASUC) Vol.4, No.1, February 2013, “Opinion Mining and SentimentAnalysis –An Assessment of Peoples’ Belief: A Survey”S Padmaja andProf. S Sameen Fatima.
  14. Hsinchun Chen, Roger H. L. Chiang(2012), BUSINESSINTELLIGENCE AND ANALYTICS: FROM BIG DATA TO BIGIMPACT, MIS Quarterly Vol. 36 No. 4, pp. 1165-1188/December 2012
  15. Ferguson, Rebecca (2012). Learning analytics: drivers, developments andchallenges. International Journal of Technology Enhanced Learning,4(5/6) pp. 304–317
  16. Vasant Dhar(2012), Data Science and Prediction, May2012,http://hdl.handle.net/2451/31553
  17. Chid Apte(2010), The Role of Machine Learning in BusinessOptimization, in Proceedings of the 27th International Conference onMachine Learning, Haifa, Israel, 2010.
  18. Chiang, R. H. L., Goes, P., and Stohr, E. A. 2012. Business intelligenceand analytics education, and program development: A uniqueopportunity for the information systems discipline. ACM Trans.Manage. Inf. Syst.3, 3, Article 12 (October 2012), 13 pages.
  19. Ranjit Bose (2008), Advanced Analytics, opportunities and challenges,industrial management & data systems ISSN:0263-5577
  20. Sharma, R., Mithas, S. and Kankanhalli, A. (2014). Transformingdecision-making processes: a research agenda for understanding theimpact of business analytics on organisations. European Journal ofInformation Systems, 23 (4), 433-441.
  21. Zack Jourdan, R. Kelly Rainer, and Thomas E. Marshall, BusinessIntelligence: An Analysis of the Literature, Information SystemsManagement, 25: 121–131Copyright b© Taylor & Francis Group, LLCISSN: 1058-0530 print/1934-8703
  22. Sonosy, O. A., Rady, S., Badr, N. L., & Hashem, M. (2016). A study ofspatial machine learning for business behavior prediction in locationbased social networks. 2016 11th International Conference on ComputerEngineering & Systems (ICCES). doi:10.1109/icces.2016.7822012.
  23. Singh B., Kushwaha N., & Vyas O. P. (2016). An interpretation ofsentiment analysis for enrichment of Business Intelligence. 2016 IEEERegion 10 Conference (TENCON). doi:10.1109/tencon.2016.7847950
  24. ] Chen J., Huang C., & Cheng C. (2016). The monitoring system ofBusiness support system with emergency prediction based on machinelearning approach. 2016 18th Asia-Pacific Network Operations andManagement Symposium(APNOMS).doi:10.1109/apnoms.2016.7737239
  25. Ghiassi, M., Zimbra, D., & Lee, S. (2016). Targeted Twitter Sentiment Analysis for Brands Using Supervised Feature Engineering and the Dynamic Architecture for Artificial Neural Networks. Journal of Management Information Systems, 33(4), 1034-1058.doi:10.1080/07421222.2016.1267526
  26. Ghiassi, M., Zimbra, D., & Lee, S. (2016). Targeted Twitter Sentiment Analysis for Brands Using Supervised Feature Engineering and the Dynamic Architecture for Artificial Neural Networks. Journal of Management Information Systems, 33(4), 1034-1058.doi:10.1080/07421222.2016.1267526
  27. Kumar, A., & Joshi, A. (2017). Ontology driven Sentiment Analysis onSocial Web for Government Intelligence. Proceedings of the SpecialCollection on eGovernment Innovations in India - ICEGOV 17.doi:10.1145/3055219.3055229
  28. Swain, A. K., & Cao, R. Q. (2017). Using sentiment analysis to improvesupply chain intelligence. Information Systems Frontiers. doi:10.1007/s10796-017-9762-2
  29. V. Singh, R. Adhikari, and D. Mahata, “A clustering and opinion miningapproach to socio-political analysis of the blogosphere,” inComputational Intelligence and Computing Research (ICCIC), 2010 IEEE International Conference on, Dec 2010, pp. 1–4.
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