A regression approach to valence-arousal ratings of words from word embedding

Traditional affective lexicons are mainly based on discrete classes, such as positive, happiness, sadness, which may limit its expressive power compared to the dimensional representation in which affective meanings are expressed through continuous numerical values on multiple dimensions, such as valence-arousal. Traditional methods for acquiring dimensional lexicons are mainly based on time-consuming manual annotation. In this paper, we propose a regression-based method to automatically infer the valence-arousal ratings of words via their word embedding. This method is based on the assumption that different features in word embedding contribute differently to different affective meanings. Experiments on three valence-arousal lexicons show that our method outperforms the state-of-the-art method on all the lexicons under four different evaluation metrics. Our model also has superior computation advantage over the state-of-the-art model.