Paper: Emotion Classification Using Massive Examples Extracted from the Web

ACL ID C08-1111
Title Emotion Classification Using Massive Examples Extracted from the Web
Venue International Conference on Computational Linguistics
Session Main Conference
Year 2008
Authors

In this paper, we propose a data-oriented method for inferring the emotion of a speaker conversing with a dialog system from the semantic content of an utterance. We first fully automatically obtain a huge collection of emotion-provoking event in- stances from the Web. With Japanese cho- sen as a target language, about 1.3 million emotion provoking event instances are ex- tracted using an emotion lexicon and lexi- cal patterns. We then decompose the emo- tion classification task into two sub-steps: sentiment polarity classification (coarse- grained emotion classification), and emo- tion classification (fine-grained emotion classification). For each subtask, the collection of emotion-proviking event in- stances is used as labelled examples to train a classifier. The results of our ex- perim...