Learning to recognize affective polarity in Similes

Abstract

A simile is a comparison between two essentially unlike things, such as "Jane swims like a dolphin". Similes often express a positive or negative sentiment toward something, but recognizing the polarity of a simile can depend heavily on world knowledge. For example, "memory like an elephant" is positive, but "memory like a sieve " is negative. Our research explores methods to recognize the polarity of similes on Twitter. We train classifiers using lexical, semantic, and sentiment features, and experiment with both manually and automatically generated training data. Our approach yields good performance at identifying positive and negative similes, and substantially outperforms existing sentiment resources.