Abstract
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.
This work was done while the first author was an employee of Clairvoyance Corporation.
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Grefenstette, G., Qu, Y., Evans, D.A., Shanahan, J.G. (2006). Validating the Coverage of Lexical Resources for Affect Analysis and Automatically Classifying New Words along Semantic Axes. In: Shanahan, J.G., Qu, Y., Wiebe, J. (eds) Computing Attitude and Affect in Text: Theory and Applications. The Information Retrieval Series, vol 20. Springer, Dordrecht. https://doi.org/10.1007/1-4020-4102-0_9
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DOI: https://doi.org/10.1007/1-4020-4102-0_9
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