| Deriving marketing intelligence from online discussion |
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International Conference on Knowledge Discovery and Data Mining
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Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
table of contents
Chicago, Illinois, USA
SESSION: Industry/government track paper
table of contents
Pages: 419 - 428
Year of Publication: 2005
ISBN:1-59593-135-X
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Authors
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Natalie Glance
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Intelliseek Applied Research Center, Pittsburgh, PA
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Matthew Hurst
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Intelliseek Applied Research Center, Pittsburgh, PA
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Kamal Nigam
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Intelliseek Applied Research Center, Pittsburgh, PA
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Matthew Siegler
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Intelliseek Applied Research Center, Pittsburgh, PA
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Robert Stockton
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Intelliseek Applied Research Center, Pittsburgh, PA
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Takashi Tomokiyo
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Intelliseek Applied Research Center, Pittsburgh, PA
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Downloads (6 Weeks): 42, Downloads (12 Months): 275, Citation Count: 15
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ABSTRACT
Weblogs and message boards provide online forums for discussion that record the voice of the public. Woven into this mass of discussion is a wide range of opinion and commentary about consumer products. This presents an opportunity for companies to understand and respond to the consumer by analyzing this unsolicited feedback. Given the volume, format and content of the data, the appropriate approach to understand this data is to use large-scale web and text data mining technologies.This paper argues that applications for mining large volumes of textual data for marketing intelligence should provide two key elements: a suite of powerful mining and visualization technologies and an interactive analysis environment which allows for rapid generation and testing of hypotheses. This paper presents such a system that gathers and annotates online discussion relating to consumer products using a wide variety of state-of-the-art techniques, including crawling, wrapping, search, text classification and computational linguistics. Marketing intelligence is derived through an interactive analysis framework uniquely configured to leverage the connectivity and content of annotated online discussion.
REFERENCES
Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.
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CITED BY 15
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Jure Leskovec , Andreas Krause , Carlos Guestrin , Christos Faloutsos , Jeanne VanBriesen , Natalie Glance, Cost-effective outbreak detection in networks, Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining, August 12-15, 2007, San Jose, California, USA
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Rui Cai , Jiang-Ming Yang , Wei Lai , Yida Wang , Lei Zhang, iRobot: an intelligent crawler for web forums, Proceeding of the 17th international conference on World Wide Web, April 21-25, 2008, Beijing, China
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Nayer Wanas , Motaz El-Saban , Heba Ashour , Waleed Ammar, Automatic scoring of online discussion posts, Proceeding of the 2nd ACM workshop on Information credibility on the web, October 30-30, 2008, Napa Valley, California, USA
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Jiang-Ming Yang , Rui Cai , Yida Wang , Jun Zhu , Lei Zhang , Wei-Ying Ma, Incorporating site-level knowledge to extract structured data from web forums, Proceedings of the 18th international conference on World wide web, April 20-24, 2009, Madrid, Spain
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