Emotion scanning of the world’s best colleges using real-time tweets

Abstract

With the advent of technology and the popularity of social media, people have started sharing their points of view with the masses. Views of the people matter a lot to analyze the effect of dissemination of information in a large network such as Twitter. Emotion analysis of the tweets helps to determine the polarity and inclination of the vast majority toward a specific topic, issue, or entity. During elections, film promotions, brand endorsements/advertisements, and in many other areas, the applications of such research can be easily observed these days. The paper proposes performing the emotion scanning of people’s opinions on the three top colleges and universities of the world according to various surveys and indices (Harvard, MIT, and Stanford) using real-time Twitter data. Also, a comparison has been drawn between the accuracies of a few machine learning techniques used, for instance, K-nearest neighbor (KNN), support vector machines (SVM), and Naïve Bayes.