Deriving marketing intelligence from online discussion

  • Authors:

  • Natalie Glance;Matthew Hurst;Kamal Nigam;Matthew Siegler;Robert Stockton;Takashi Tomokiyo

  • Affiliations:

  • Intelliseek Applied Research Center, Pittsburgh, PA;Intelliseek Applied Research Center, Pittsburgh, PA;Intelliseek Applied Research Center, Pittsburgh, PA;Intelliseek Applied Research Center, Pittsburgh, PA;Intelliseek Applied Research Center, Pittsburgh, PA;Intelliseek Applied Research Center, Pittsburgh, PA

  • Venue:
  • Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
  • Year:
  • 2005

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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.