skip to main content
10.1145/2034617.2034621acmotherconferencesArticle/Chapter ViewAbstractPublication Pagesmocr-andConference Proceedingsconference-collections
research-article

Acquiring competitive intelligence from social media

Published:17 September 2011Publication History

ABSTRACT

Competitive intelligence is the art of defining, gathering and analyzing intelligence about competitor's products, promotions, sales etc. from external sources. The Web comes across as an important source for gathering competitive intelligence. News, blogs, as well as social media not only provide competitors information but also provide direct comparison of customer behaviors with respect to different verticals among competing organizations. This paper discusses methodologies to obtain competitive intelligence from different types of web resources including social media using a wide array of text mining techniques. It provides some results from case-studies to show how the gathered information can be integrated with structured data and used to explain business facts and thereby adopted for future decision making.

References

  1. Ben Gilad and Jan Herring, "CI Certification -- Do We Need It?", Competitive Intelligence Magazine, 2001, 4(2), 28--31.Google ScholarGoogle Scholar
  2. D. Blenkhorn and C. S. Fleisher, Competitive Intelligence and Global Business. Westport, CT: Praeger, 2005Google ScholarGoogle Scholar
  3. Stephen D. Rappaport, "Listen First -- Turning Social Media Conversations into Business Advantage", John Wiley & Sons, Inc., 2011.Google ScholarGoogle Scholar
  4. José Palazzo M. de Oliveira et al., Applying Text Mining on Electronic Messages for Competitive Intelligence, 5th International Conference on Electronic Commerce and Web Technologies, EC-Web 2004, Zaragoza, Spain 2004.Google ScholarGoogle Scholar
  5. A. Zanasi, Text Mining and its Applications to Intelligence, CRM and Knowledge Management, Southampton: WIT Press, 2005. Google ScholarGoogle ScholarDigital LibraryDigital Library
  6. Yuen-Hsien Tseng, Chi-Jen Lin and Yu-I Lin, Text mining techniques for patent analysis, Journal of Information Process & Management, Vol. 43, 5, 2007. Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. Bing Liu, Sentiment analysis and subjectivity, in Handbook of Natural Language Processing, Second Edition, (editors: N. Indurkhya and F. J. Damerau), 2010Google ScholarGoogle Scholar
  8. Bo Pang and Lillian Lee, Opinion Mining and Sentiment Analysis, Foundations and Trends in Information Retrieval, Vol. 2, 1--2, 2008. Google ScholarGoogle ScholarDigital LibraryDigital Library
  9. Nitin Jindal and Bing Liu, Identifying comparative sentences in text documents, SIGIR '06 - Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval, 2006. Google ScholarGoogle ScholarDigital LibraryDigital Library
  10. Lipika Dey and Sk. Mirajul Haque, Opinion Mining from noisy text data, International Journal of Document Analysis and Recognition, September 2009. Google ScholarGoogle ScholarDigital LibraryDigital Library
  11. Gautam Shroff, Puneet Agarwal and Lipika Dey, Enterprise Information Fusion for Real-time Business Intelligence, FUSION 2011, Chicago, July, 2011.Google ScholarGoogle Scholar
  12. M. Missikoff, P. Velardi, P. Fabriani, Text mining techniques to automatically enrich a domain ontology, Applied ntelligence 18 (3) (2003) 323--340. Google ScholarGoogle ScholarDigital LibraryDigital Library
  13. Missikoff, M., Velardi P., and Peterson, L. L. Reasoning about naming systems. ACM Trans. Program. Lang. Syst. 15, 5, 1993, 795--825. Google ScholarGoogle ScholarDigital LibraryDigital Library
  14. Blei et al., 2003} D. M. Blei, A. Y. Ng, and M. I. Jordan, "Latent dirichlet allocation," The Journal of Machine Learning Research, vol.3, pp. 993--1022, 2003. Google ScholarGoogle ScholarDigital LibraryDigital Library
  15. Arpit Khurdiya, Lipika Dey, Nidhi Raj and Sk. M. Haque, Multi-perspective linking of news articles within a repository, to be presented at IJCAI 2011, Barcelona, Spain, July, 2011. Google ScholarGoogle ScholarDigital LibraryDigital Library
  16. Raymond Y. K. Lau and Wenping Zhang, Semi-supervised Statistical Inference for Business Entities Extraction and Business Relations Discovery, SIGIR 2011 workshop, July 28, Beijing, China, 2011.Google ScholarGoogle Scholar
  17. Henning Baars and Hans-George Kemper, Management Support with Structured and Unstructured Data---An Integrated Business Intelligence Framework, Information Systems Management, 25: 132--148, 2008. Google ScholarGoogle ScholarDigital LibraryDigital Library

Recommendations

Comments

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Sign in
  • Published in

    cover image ACM Other conferences
    MOCR_AND '11: Proceedings of the 2011 Joint Workshop on Multilingual OCR and Analytics for Noisy Unstructured Text Data
    September 2011
    144 pages
    ISBN:9781450306850
    DOI:10.1145/2034617

    Copyright © 2011 ACM

    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

    Publisher

    Association for Computing Machinery

    New York, NY, United States

    Publication History

    • Published: 17 September 2011

    Permissions

    Request permissions about this article.

    Request Permissions

    Check for updates

    Qualifiers

    • research-article

PDF Format

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader