Computing Attitude and Affect in Text: Theory and Applications

Volume 20 of the series The Information Retrieval Series pp 93-107

Validating the Coverage of Lexical Resources for Affect Analysis and Automatically Classifying New Words along Semantic Axes

  • Gregory GrefenstetteAffiliated withCommissariat à l’Energie Atomique, Centre de Fontenay-aux-Roses, CEA/LIST/DTSI/SCRI/LIC2M
  • , Yan QuAffiliated withClairvoyance Corporation
  • , David A. EvansAffiliated withClairvoyance Corporation
  • , James G. ShanahanAffiliated withTurn Inc

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In addition to factual content, many texts contain an emotional dimension. This emotive, or affect, dimension has not received a great amount of attention in computational linguistics until recently. However, now that messages (including spam) have become more prevalent than edited texts (such as newswire), recognizing this emotive dimension of written text is becoming more important. One resource needed for identifying affect in text is a lexicon of words with emotion-conveying potential. Starting from an existing affect lexicon and lexical patterns that invoke affect, we gathered a large quantity of text to measure the coverage of our existing lexicon. This chapter reports on our methods for identifying new candidate affect words and on our evaluation of our current affect lexicons. We describe how our affect lexicon can be extended based on results from these experiments.


affect lexicon emotion lexicon discovery semantic axes