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 data on the emotion identification task. To overcome this bottleneck, we have automatically created a large emotion-labeled dataset (of about 3,700 tweets) by harnessing emotion-related hashtags available in the tweets. Extant studies on emotion identification lack comprehensive coverage of “emotional situations” because they use relatively small training datasets. 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.
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