Paper: An Exploration of Features for Recognizing Word Emotion

ACL ID C10-1104
Title An Exploration of Features for Recognizing Word Emotion
Venue International Conference on Computational Linguistics
Session Main Conference
Year 2010

Emotion words have been well used as the most obvious choice as feature in the task of textual emotion recognition and auto- matic emotion lexicon construction. In this work, we explore features for rec- ognizing word emotion. Based on Ren- CECps (an annotated emotion corpus) and MaxEnt (Maximum entropy) model, sev- eral contextual features and their com- bination have been experimented. Then PLSA (probabilistic latent semantic anal- ysis) is used to get semantic feature by clustering words and sentences. The ex- perimental results demonstrate the effec- tiveness of using semantic feature for word emotion recognition. After that, “word emotion components” is proposed to describe the combined basic emotions in a word. A significant performance improvement over contextual and seman- tic ...