Paper: Using Roget's Thesaurus for Fine-grained Emotion Recognition

ACL ID I08-1041
Title Using Roget's Thesaurus for Fine-grained Emotion Recognition
Venue International Joint Conference on Natural Language Processing
Session Main Conference
Year 2008

Recognizing the emotive meaning of text can add another dimension to the under- standing of text. We study the task of automatically categorizing sentences in a text into Ekman’s six basic emotion cate- gories. We experiment with corpus-based features as well as features derived from two emotion lexicons. One lexicon is automatically built using the classification system of Roget’s Thesaurus, while the other consists of words extracted from WordNet-Affect. Experiments on the data obtained from blogs show that a combina- tion of corpus-based unigram features with emotion-related features provides superior classification performance. We achieve F- measure values that outperform the rule- based baseline method for all emotion classes.