Seeing the best and worst of everything on the web with a two-level, feature-rich affect lexicon

Tony Veale

Abstract: Affect lexica are useful for sentiment analysis because they map words (or senses) onto sentiment ratings. However, few lexica explain their ratings, or provide sufficient feature richness to allow a selective "spin"" to be placed on a word in context. Since an affect lexicon aims to capture the affect of a word or sense in its most stereotypical usage, it should be grounded in explicit stereotype representations of each word's most salient properties and behaviors. We show here how to acquire a large stereotype lexicon from Web content, and further show how to determine sentiment ratings for each entry in the lexicon, both at the level of properties and behaviors and at the level of stereotypes. Finally, we show how the properties of a stereotype can be segregated on demand, to place a positive or negative spin on a word in context.