Embedding Learning of Figurative Phrases for Emotion Classification in Micro-Blog Texts

Abstract

Figurative phrases such as idioms are a type of Multi-Word Expressions (MWE) that possess a specialized meaning, which is independent and different from the literal meaning of the constituent words. Figurative language is widely used to express emotions and are very predominant in micro-blog data.Therefore, an efficient model of emotion categorization for micro-blogs should be able to correctly represent the instances of figurative phrases in the data. However, due to their non-compositional nature, the phrasal representation of figurative language cannot be directly obtained from the constituent words and hence this requires novel approaches for addressing the problem of modeling figurative phrases in micro-blogs. Most of the existing methods of modeling figurative idiomatic phrases in traditional text data use the broader textual context available for better results. However, in case of micro-blog data, such large context is not available due to very short length of text, which poses an additional challenge. Given the need to model figurative language for emotion classification, this paper develops the novel idea of Emotion Sensitive Figurative Phrase Embedding (ESFPE) to model idiomatic phrases in micro-blog data and show upto 14% improvement in emotion classification performance over baseline. To the best of our knowledge, this is the first work towards figurative phrase modeling for emotion classification in micro-blog text.