|Title||Harnessing Twitter ‘Big Data’ for Automatic Emotion Identification|
|Publication Type||Conference Proceedings|
|Year of Publication||2012|
|Authors||Wenbo Wang, Lu Chen, Krishnaprasad Thirunarayan, Amit Sheth|
|Conference Name||2012 International Conference on Social Computing (SocialCom)|
|Conference Location||Amsterdam, The Netherlands|
|Keywords||automatic emotion identification task, bigrams, comprehensive coverage, emotion-bearing words, emotion-related hashtags, emotional situations, feature combinations, harnessing Twitter big data, large emotion-labeled dataset, machine learning algorithms, parts-of-speech information, people behaviors, people emotions, small training datasets, training data, unigrams, user-generated 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 data on the emotion identification task. Our experiments demonstrate that a combination of unigrams, big rams, sentiment/emotion-bearing words, and parts-of-speech information is most effective for gleaning emotions. The highest accuracy (65.57%) is achieved with a training data containing about 2 million tweets.