@inproceedings{byrkjeland-etal-2018-ternary,
title = "Ternary {T}witter Sentiment Classification with Distant Supervision and Sentiment-Specific Word Embeddings",
author = {Byrkjeland, Mats and
G{\o}rvell de Lichtenberg, Frederik and
Gamb{\"a}ck, Bj{\"o}rn},
editor = "Balahur, Alexandra and
Mohammad, Saif M. and
Hoste, Veronique and
Klinger, Roman",
booktitle = "Proceedings of the 9th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis",
month = oct,
year = "2018",
address = "Brussels, Belgium",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W18-6215/",
doi = "10.18653/v1/W18-6215",
pages = "97--106",
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."
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%0 Conference Proceedings
%T Ternary Twitter Sentiment Classification with Distant Supervision and Sentiment-Specific Word Embeddings
%A Byrkjeland, Mats
%A Gørvell de Lichtenberg, Frederik
%A Gambäck, Björn
%Y Balahur, Alexandra
%Y Mohammad, Saif M.
%Y Hoste, Veronique
%Y Klinger, Roman
%S Proceedings of the 9th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis
%D 2018
%8 October
%I Association for Computational Linguistics
%C Brussels, Belgium
%F byrkjeland-etal-2018-ternary
%X 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.
%R 10.18653/v1/W18-6215
%U https://aclanthology.org/W18-6215/
%U https://doi.org/10.18653/v1/W18-6215
%P 97-106
Markdown (Informal)
[Ternary Twitter Sentiment Classification with Distant Supervision and Sentiment-Specific Word Embeddings](https://aclanthology.org/W18-6215/) (Byrkjeland et al., WASSA 2018)
ACL