Emotion Analysis of Social Media Content

User generated content on Twitter (produced at an enormous rate of 340 million tweets per day) provides a rich source for gleaning people's emotions, which is necessary for deeper understanding of people's behaviors and actions. Extant studies on emotion identification lack comprehensive coverage of "emotional situations" because they use relatively small training datasets. To overcome this bottleneck, we have automatically created a large emotion-labeled dataset (of about 2.5 million tweets) by harnessing emotion-related hashtags available in the tweets. We have applied two different machine learning algorithms for emotion identification, to study the effectiveness of various feature combinations as well as the effect of the size of the training dataset on the emotion identification task. More content will be added soon


Wenbo Wang, Lu Chen, Krishnaprasad Thirunarayan and Amit P. Sheth. Harnessing Twitter ‘Big Data’ for Automatic Emotion Identification. In Proceedings of International Conference on Social Computing (SocialCom), 2012.