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Deriving marketing intelligence from online discussion
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Source International Conference on Knowledge Discovery and Data Mining archive
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
Authors
Natalie Glance  Intelliseek Applied Research Center, Pittsburgh, PA
Matthew Hurst  Intelliseek Applied Research Center, Pittsburgh, PA
Kamal Nigam  Intelliseek Applied Research Center, Pittsburgh, PA
Matthew Siegler  Intelliseek Applied Research Center, Pittsburgh, PA
Robert Stockton  Intelliseek Applied Research Center, Pittsburgh, PA
Takashi Tomokiyo  Intelliseek Applied Research Center, Pittsburgh, PA
Sponsors
SIGKDD: ACM Special Interest Group on Knowledge Discovery in Data
ACM: Association for Computing Machinery
Publisher
ACM  New York, NY, USA
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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

Collaborative Colleagues:
Natalie Glance: colleagues
Matthew Hurst: colleagues
Kamal Nigam: colleagues
Matthew Siegler: colleagues
Robert Stockton: colleagues
Takashi Tomokiyo: colleagues