Enhanced Sentiment Learning Using Twitter Hashtags and Smileys
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 types of un- tagged sentences. The quality of the senti- ment identification was also confirmed by human judges. We also explore dependen- cies and overlap between different sen- timent types represented by smileys and Twitter hashtags.