Targeting Sentiment Expressions through Supervised Ranking of Linguistic Configurations

@inproceedings{Kessler2009TargetingSE,
  title={Targeting Sentiment Expressions through Supervised Ranking of Linguistic Configurations},
  author={Jason S. Kessler and Nicolas Nicolov},
  booktitle={ICWSM},
  year={2009}
}
User generated content is extremely valuable for mining market intelligence because it is unsolicited. We study the problem of analyzing users’ sentiment and opinion in their blog, message board, etc. posts with respect to topics expressed as a search query. In the scenario we consider the matches of the search query terms are expanded through coreference and meronymy to produce a set of mentions. The mentions are contextually evaluated for sentiment and their scores are aggregated (using a… CONTINUE READING
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