Paper: Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification

ACL ID P14-1146
Title Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2014
Authors

We present a method that learns word em- bedding for Twitter sentiment classifica- tion in this paper. Most existing algorithm- s for learning continuous word represen- tations typically only model the syntactic context of words but ignore the sentimen- t of text. This is problematic for senti- ment analysis as they usually map word- s with similar syntactic context but oppo- site sentiment polarity, such as good and bad, to neighboring word vectors. We address this issue by learning sentiment- specific word embedding (SSWE), which encodes sentiment information in the con- tinuous representation of words. Specif- ically, we develop three neural networks to effectively incorporate the supervision from sentiment polarity of text (e.g. sen- tences or tweets) in their loss function- s. To obta...