Kernel models for affective lexicon creation

Nikos Malandrakis, Alexandros Potamianos, Elias Iosif, Shrikanth Narayanan

Research output: Contribution to journalConference articlepeer-review

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 languageEnglish
Pages (from-to)2977-2980
Number of pages4
JournalProceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH
Publication statusPublished - 2011
Externally publishedYes
Event12th Annual Conference of the International Speech Communication Association, INTERSPEECH 2011 - Florence, Italy
Duration: 27 Aug 201131 Aug 2011

Keywords

  • Affect
  • Affective lexicon
  • Emotion
  • Language understanding

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