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
Bibkey:
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)
Copy Citation:
PDF:
https://preview.aclanthology.org/naacl-24-ws-corrections/W18-6215.pdf