Paper: Building Large-Scale Twitter-Specific Sentiment Lexicon : A Representation Learning Approach

ACL ID C14-1018
Title Building Large-Scale Twitter-Specific Sentiment Lexicon : A Representation Learning Approach
Venue International Conference on Computational Linguistics
Session Main Conference
Year 2014
Authors

In this paper, we propose to build large-scale sentiment lexicon from Twitter with a representation learning approach. We cast sentiment lexicon learning as a phrase-level sentiment classification task. The challenges are developing effective feature representation of phrases and obtaining training data with minor manual annotations for building the sentiment classifier. Specifical- ly, we develop a dedicated neural architecture and integrate the sentiment information of tex- t (e.g. sentences or tweets) into its hybrid loss function for learning sentiment-specific phrase embedding (SSPE). The neural network is trained from massive tweets collected with positive and negative emoticons, without any manual annotation. Furthermore, we introduce the Urban Dictionary to expand a small number of...