Mats Byrkjeland


2018

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Ternary Twitter Sentiment Classification with Distant Supervision and Sentiment-Specific Word Embeddings
Mats Byrkjeland | Frederik Gørvell de Lichtenberg | Björn Gambäck
Proceedings of the 9th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis

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.