Abstract
Emotion recognition algorithms for spoken dialogue applications typically employ lexical models that are trained on labeled in-domain data. In this paper, we propose a domain-independent approach to affective text modeling that is based on the creation of an affective lexicon. Starting from a small set of manually annotated seed words, continuous valence ratings for new words are estimated using semantic similarity scores and a kernel model. The parameters of the model are trained using least mean squares estimation. Word level scores are combined to produce sentence-level scores via simple linear and non-linear fusion. The proposed method is evaluated on the SemEval news headline polarity task and on the ChIMP politeness and frustration detection dialogue task, achieving state-of-the-art results on both. For politeness detection, best results are obtained when the affective model is adapted using in domain data. For frustration detection, the domain-independent model and non-linear fusion achieve the best performance.
Original language | English |
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Pages (from-to) | 2977-2980 |
Number of pages | 4 |
Journal | Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH |
Publication status | Published - 2011 |
Externally published | Yes |
Event | 12th Annual Conference of the International Speech Communication Association, INTERSPEECH 2011 - Florence, Italy Duration: 27 Aug 2011 → 31 Aug 2011 |
Keywords
- Affect
- Affective lexicon
- Emotion
- Language understanding