Enhanced Sentiment Learning Using Twitter Hashtags and Smileys

A supervised sentiment classification framework which is based on data from Twitter, a popular microblogging service, is proposed, utilizing 50 Twitter tags and 15 smileys as sentiment labels, allowing identification and classification of diverse sentiment types of short texts.

Abstract

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. 1