Abstract
Dimensional approach has become a popular method in sentiment analysis because it represents emotions as continuous numerical values in multiple dimensions, such as valence-arousal (VA) space [1], therefore it can provide more fine-grained sentiment analysis compared to traditional categorical approach which represents affective states in discrete classes. However, affective lexicons and corpora with VA ratings are very rare and it makes dimensional approach hard to use in reality. This paper describes using KNN algorithm to predict VA ratings for new affective lexicons by leveraging word embedding and available affective corpora with VA ratings.
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