Paper: Probabilistic Sense Sentiment Similarity through Hidden Emotions

ACL ID P13-1097
Title Probabilistic Sense Sentiment Similarity through Hidden Emotions
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2013
Authors

Sentiment Similarity of word pairs reflects the distance between the words regarding their underlying sentiments. This paper aims to in- fer the sentiment similarity between word pairs with respect to their senses. To achieve this aim, we propose a probabilistic emotion- based approach that is built on a hidden emo- tional model. The model aims to predict a vec- tor of basic human emotions for each sense of the words. The resultant emotional vectors are then employed to infer the sentiment similarity of word pairs. We apply the proposed ap- proach to address two main NLP tasks, name- ly, Indirect yes/no Question Answer Pairs in- ference and Sentiment Orientation prediction. Extensive experiments demonstrate the effec- tiveness of the proposed approach.