Modelling and visualising emotions in Twitter feeds

Predictive analytics on Twitter feeds is becoming a popular field for research. A tweet holds wealth of information on how an individual express and communicates their feelings and emotions within their social network. Large-scale mining of tweets will not only help in capturing an individual's emotion but also the emotions of a larger group. In this study, an emotion-based classification scheme has been proposed. By training the naïve Bayes multinomial and the random forest classifiers on different training datasets, emotion classification was performed on the test dataset containing tweets related to the 2016 US presidential election. Upon classifying the tweets in the test dataset to one of the four basic emotion types: anger, happy, sadness and surprise, and by determining the sentiments of the people, we have tried to portray the flux in the emotional landscape of the people towards the presidential candidates in the 2016 US election.