A novel social media competitive analytics framework with sentiment benchmarks

Information and Management

Volumn 52, Issue 7, 2015, Pages 801-812

  He, Wu ^a   Wu, Harris ^a   Yan, Gongjun ^b   Akula, Vasudeva ^c   Shen, Jiancheng ^a  

^a OLD DOMINION UNIVERSITY   (United States)

^b UNIVERSITY OF SOUTHERN INDIANA   (United States)

^c Head of Business Consulting VOZIQ   (United States)

Author keywords

Big data; Competitive analytics; Marketing intelligence; Sentiment analysis; Sentiment benchmarks; Social media; Social media analytics; Social media monitoring; Text mining; User generated data

Indexed keywords

BIG DATA; COMMERCE; COMPETITION; DATA MINING; MARKETING; NATURAL LANGUAGE PROCESSING SYSTEMS; SALES;

COMPETITIVE ANALYTICS; MARKETING INTELLIGENCES; SENTIMENT ANALYSIS; SOCIAL MEDIA; SOCIAL MEDIA ANALYTICS; SOCIAL MEDIA MONITORING; TEXT MINING; USER-GENERATED;

SOCIAL NETWORKING (ONLINE);

EID: 84942297531     PISSN: 03787206     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.im.2015.04.006     Document Type: Article

References (52)

1. 20 Newsgroups The 20 Newsgroups Data Set 2008 Retrieved from (http://qwone.com/jason/20Newsgroups, at January 2008)
2. M. Abdous, and W. He Using text mining to uncover students' technology-related problems in live video streaming Br. J. Educ. Technol. 40 5 2011 40 49
3. A.S. Abrahams, W. Fan, J. Jiao, G.A. Wang, and Z. Zhang An integrated text analytic framework for product defect discovery Prod. Oper. Manage. 2015 10.1111/poms.12303
4. S. Abrahams, J. Jiao, W. Fan, G.A. Wang, and Z. Zhang What's buzzing in the blizzard of buzz? Automotive component isolation in social media postings Decis. Support Syst. 55 4 2013 871 882
5. S. Abrahams, J. Jiao, G.A. Wang, and W. Fan Vehicle defect discovery from social media Decis. Support Syst. 54 1 2012 87 97
6. G. Barbier, and H. Liu Data mining in social media Social Network Data Analytics 2011 Springer US 327 352
7. R. Bellman, R.E. Bellman, R.E. Bellman, and R.E. Bellman Adaptive Control Processes: A Guided Tour vol. 4 1961 Princeton University Press Princeton, NJ
8. J. Bollen, H. Mao, and X. Zeng Twitter mood predicts the stock market J. Comput. Sci. 2 1 2011 1 8
9. R. Bose Competitive intelligence process and tools for intelligence analysis Ind. Manage. Data Syst. 108 4 2008 510 528
10. W.B. Cavnar, and J.M. Trenkle N-gram-based text categorization Ann Arbor, MI 48113 2 1994 161 175
11. M. Chau, and J. J. Xu Business intelligence in blogs: understanding consumer interactions and communities MIS Q. 36 4 2012 1189 1216
12. H. Chen, M. Chau, and D. Zeng CI Spider: a tool for competitive intelligence on the Web Decis. Support Syst. 34 1 2002 1 17
13. H. Chen, R.H. Chiang, and V.C. Storey Business intelligence and analytics: from big data to big impact MIS Q. 36 4 2012 1165 1188
14. L. Dey, S.M. Haque, A. Khurdiya, and G. Shroff Acquiring competitive intelligence from social media Proceedings of the 2011 Joint Workshop on Multilingual OCR and Analytics for Noisy Unstructured Text Data ACM 2011 3
15. W. Duan, Q. Cao, Y. Yu, and S. Levy Mining online user-generated content: using sentiment analysis technique to study hotel service quality Proceedings of the 46th Hawaii International Conference on System Sciences 2013 3119 3128
16. W. Fan, and M.D. Gordon The power of social media analytics Commun. ACM 57 6 2014 74 81
17. M.U. Fayyad, G. Piatesky-Shapiro, P. Smuth, and R. Uthurusamy Advances in Knowledge Discovery and Data Mining 1996 AAAI/MIT Press Palo Alto, California
18. G. Governatori, and R. Iannella A modeling and reasoning framework for social networks policies Enterp. Inf. Syst. 5 1 2011 145 167
19. W. He, S.H. Zha, and L. Li Social media competitive analysis and text mining: a case study in the pizza industry Int. J. Inf. Manage. 33 3 2013 464 472
20. W. He Examining students' online interaction in a live video streaming environment using data mining and text mining Comput. Hum. Behav. 29 1 2013 90 102
21. W. He Improving user experience with case-based reasoning systems using text mining and Web 2.0 Expert Syst. Appl. 40 2 2013 500 507
22. J. Hung Trends of E-Learning Research from 2000 to 2008: use of Text Mining and Bibliometrics Br. J. Educ. Technol. 43 1 2012 5 16
23. J. Hung, and K. Zhang Revealing online learning behaviors and activity patterns and making predictions with data mining techniques in online teaching MERLOT J. Online Learn. Teach. 4 4 2008 426 437
24. L. Kahaner Competitive Intelligence: How to Gather, Analyze and Use Information to Move Your Business to the Top 1998 Touchstone New York, NY
25. Y. Kim Weighted order-dependent clustering and visualization of web navigation patterns Decis. Support Syst. 43 4 2007 1630 1645
26. A. Klein, O. Altuntas, M. Riekert, and V. Dinev A combined approach for extracting financial instrument-specific investor sentiment from weblogs Wirtschaftsinformatik Proceedings 2013 44
27. A. Kloptchenko, T. Eklund, J. Karlsson, B. Back, H. Vanharanta, and A. Visa Combining data and text mining techniques for analyzing financial reports Intell. Syst. Acc. Finance Manage. 12 1 2004 29 41
28. H. Lee, K. Choi, D. Yoo, Y. Suh, G. He, and S. S. Lee The more the worse? Mining valuable ideas with sentiment analysis for idea recommendation Proceedings of PACIS 2013 2013
29. J. Leskovec Social media analytics: tracking, modeling and predicting the flow of information through networks Proceedings of the 20th International Conference Companion on World Wide Web ACM 2011
30. N. Li, and D.D. Wu Using text mining and sentiment analysis for online forums hotspot detection and forecast Decis. Support Syst. 48 2 2010 354 368
31. H. Lin, W. Fan, and P. Chau Determinants of users' continuance of social networking sites: a self-regulation perspective Inf. Manage. 51 5 2014 595 603
32. B. Liu Sentiment analysis and subjectivity N. Indurkhya, F.J. Damerau, Handbook of Natural Language Processing 2010 Taylor and Francis Group Boca
33. B. Liu, S.G. Cao, and W. He Distributed data mining for e-business Inf. Technol. Manage. 12 1 2011 1 13
34. S. Ludwig, K. de Ruyter, M. Friedman, E.C. Brüggen, M. Wetzels, and G. Pfann More than words: the influence of affective content and linguistic style matches in online reviews on conversion rates J. Marketing 77 1 2013 87 103
35. G. Maskeri, S. Sarkar, and K. Heafield Mining business topics in source code using latent dirichlet allocation Proceedings of the First India Software Engineering Conference 2008 113 120
36. S. Morinaga, K. Yamanishi, K. Tateishi, and T. Fukushima Mining product reputations on the web Proceedings of the Eighth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining 2002 341 349
37. B. Pang, and L. Lee A sentimental education: sentiment analysis using subjectivity summarization based on minimum cuts Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics Barcelona, Spain 2004 271 278
38. B. Pang, l. Lee, and S. Vaithyanathan Thumbs up?: sentiment classification using machine learning techniques Proceedings of the ACL-02 Conference on Empirical Methods in Natural Language Processing, 10 Association for Computational Linguistics 2002
39. C. Romero, S. Ventura, and E. Garcia Data mining in course management systems: moodle case study and tutorial Comput. Educ. 51 1 2008 368 384
40. P. Ross, C. McGowan, and L. Styger A Comparison of Theory and Practice in Market Intelligence Gathering for Australian Micro-businesses and SMEs 2012 Social Science Electronic Publishing, Inc (available at SSRN 2174337)
41. T.O. Sprenger, I.M. Welpe, Tweets and trades: The information content of stock microblogs, November 1, 2010, (available at SSRN: http://ssrn.com/abstract=1702854) or http://dx.doi.org/10.2139/ssrn.1702854.
42. S. Stieglitz, and L. Dang-Xuan Social media and political communication: a social media analytics framework Soc. Netw. Anal. Min. 3 4 2013 1277 1291
43. T.S. Teo, and W.Y. Choo Assessing the impact of using the Internet for competitive intelligence Inf. Manage. 39 1 2001 67 83
44. M. Thelwall, K. Buckley, and G. Paltoglou Sentiment strength detection for the socialweb J. Am. Soc. Inf. Sci. Technol. 63 1 2012 163 173
45. K.J. Trainor, T.K. Michael, and A. Raj Effects of relational proclivity and marketing intelligence on new product development Marketing Intell. Plann. 31 7 2013 788 806
46. G.A. Wang, J. Jiao, A.S. Abrahams, W. Fan, and Z. Zhang ExpertRank: a topic-aware expert finding algorithm for online knowledge communities Decis. Support Syst. 54 3 2013 1442 1451
47. K. Xu, S.S. Liao, J. Li, and Y. Song Mining comparative opinions from customer reviews for competitive intelligence Decis. Support Syst. 50 4 2011 743 754
48. Zeng, H. Chen, R. Lusch, and S.H. Li Social media analytics and intelligence IEEE Intell. Syst. 25 6 2010 13 16
49. Z. Zheng, X. Wu, and R. Srihari Feature selection for text categorization on imbalanced data ACM SIGKDD Explorations Newsl. 6 1 2004 80 89
50. N. Zhong, Y. Li, and S. Wu Effective pattern discovery for text mining IEEE Trans. Knowl. Data Eng. 24 1 2012 30 44
51. M. Zhou, L. Lei, J. Wang, A.G.A. Wang, and W. Fan Social media adoption and corporate disclosure J. Inf. Syst. 2014 10.2308/isys-50961
52. P. Zikopoulos, K. Parasuraman, T. Deutsch, J. Giles, and D. Corrigan Harness the Power of Big Data The IBM Big Data Platform 2012 McGraw Hill Professional New York, NY