Paper: Enhanced Sentiment Learning Using Twitter Hashtags and Smileys

ACL ID C10-2028
Title Enhanced Sentiment Learning Using Twitter Hashtags and Smileys
Venue International Conference on Computational Linguistics
Session Poster Session
Year 2010
Authors

Automated identification of diverse sen- timent types can be beneficial for many NLP systems such as review summariza- tion and public media analysis. In some of these systems there is an option of assign- ing a sentiment value to a single sentence or a very short text. In this paper we propose a supervised sentiment classification framework which is based on data from Twitter, a popu- lar microblogging service. By utilizing 50 Twitter tags and 15 smileys as sen- timent labels, this framework avoids the need for labor intensive manual annota- tion, allowing identification and classifi- cation of diverse sentiment types of short texts. We evaluate the contribution of dif- ferent feature types for sentiment classifi- cation and show that our framework suc- cessfully identifies sentiment type...