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Valence-Arousal Ratings Prediction with Co-occurrence Word-embedding

Citation Author(s):
Jing Xu, Xu Yang, Bin Xu
Submitted by:
Yan Gao
Last updated:
18 November 2016 - 1:55am
Document Type:
Poster
Document Year:
2016
Event:
Presenters Name:
Yan Gao
Paper Code:
115
Categories:

Abstract 

Abstract: 

Sentiment analysis draws increasing attention of researchers in wide-ranging fields. Compared with the commonly-used categorical
approach representing affective states as a few discrete classes, the dimensional approach represents emotions as continuous numerical values in multiple dimensions, such as valence-arousal (VA) space. It can thus provide more fine-grained sentiment analysis. However, affective lexicons with VA ratings are very rare. This limitation makes the dimensional approach hard to use in reality. This study proposes a VA ratings prediction method combining word2vec and KNN.

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Dataset Files

Valence-Arousal Ratings Prediction with Co-occurrence Word-embedding.pdf

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