Abstract
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.- Anthology ID:
- E17-2090
- Volume:
- Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 2, Short Papers
- Month:
- April
- Year:
- 2017
- Address:
- Valencia, Spain
- Editors:
- Mirella Lapata, Phil Blunsom, Alexander Koller
- Venue:
- EACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 564–571
- Language:
- URL:
- https://aclanthology.org/E17-2090
- DOI:
- Cite (ACL):
- João Sedoc, Daniel Preoţiuc-Pietro, and Lyle Ungar. 2017. Predicting Emotional Word Ratings using Distributional Representations and Signed Clustering. In Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 2, Short Papers, pages 564–571, Valencia, Spain. Association for Computational Linguistics.
- Cite (Informal):
- Predicting Emotional Word Ratings using Distributional Representations and Signed Clustering (Sedoc et al., EACL 2017)
- PDF:
- https://preview.aclanthology.org/ml4al-ingestion/E17-2090.pdf