文章基本信息

标题：Predicting Emotional Word Ratings using Distributional Representations and Signed Clustering
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作者：João Sedoc ; Daniel Preoţiuc-Pietro ; Lyle Ungar 等
期刊名称：Conference on European Chapter of the Association for Computational Linguistics (EACL)
出版年度：2017
卷号：2017
页码：564-571
语种：English
出版社：ACL Anthology
摘要：Inferring the emotional content of words is important for text-based sentiment analysis, dialogue systems and psycholinguistics, but word ratings are expensive to collect at scale and across languages or domains. We develop a method that automatically extends word-level ratings to unrated words using signed clustering of vector space word representations along with affect ratings. We use our method to determine a word’s valence and arousal, which determine its position on the circumplex model of affect, the most popular dimensional model of emotion. Our method achieves superior out-of-sample word rating prediction on both affective dimensions across three different languages when compared to state-of-the-art word similarity based methods. Our method can assist building word ratings for new languages and improve downstream tasks such as sentiment analysis and emotion detection.