Ternary Twitter Sentiment Classification with Distant Supervision and Sentiment-Specific Word Embeddings
Mats Byrkjeland, Frederik Gørvell de Lichtenberg, Björn Gambäck
Abstract
The paper proposes the Ternary Sentiment Embedding Model, a new model for creating sentiment embeddings based on the Hybrid Ranking Model of Tang et al. (2016), but trained on ternary-labeled data instead of binary-labeled, utilizing sentiment embeddings from datasets made with different distant supervision methods. The model is used as part of a complete Twitter Sentiment Analysis system and empirically compared to existing systems, showing that it outperforms Hybrid Ranking and that the quality of the distant-supervised dataset has a great impact on the quality of the produced sentiment embeddings.- Anthology ID:
- W18-6215
- Volume:
- Proceedings of the 9th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis
- Month:
- October
- Year:
- 2018
- Address:
- Brussels, Belgium
- Editors:
- Alexandra Balahur, Saif M. Mohammad, Veronique Hoste, Roman Klinger
- Venue:
- WASSA
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 97–106
- Language:
- URL:
- https://aclanthology.org/W18-6215
- DOI:
- 10.18653/v1/W18-6215
- Cite (ACL):
- Mats Byrkjeland, Frederik Gørvell de Lichtenberg, and Björn Gambäck. 2018. Ternary Twitter Sentiment Classification with Distant Supervision and Sentiment-Specific Word Embeddings. In Proceedings of the 9th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis, pages 97–106, Brussels, Belgium. Association for Computational Linguistics.
- Cite (Informal):
- Ternary Twitter Sentiment Classification with Distant Supervision and Sentiment-Specific Word Embeddings (Byrkjeland et al., WASSA 2018)
- PDF:
- https://preview.aclanthology.org/naacl-24-ws-corrections/W18-6215.pdf