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

In addition to factual content, many texts contain an emotional dimension. This emotive, or affect, dimension has not received much attention in computational linguistics until recently. But now that messages (including spam) have become more prevalent than edited texts (such as newswire), recognizing this dimension 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 article reports on our methods for identifying candidate affect words and our evaluation of our current affect lexicons. We describe how our affect lexicon can be extended based on results from these experiments.