A Comparative Study of Detection and Classification of Emotions on Social Media Using SVM and Näıve Bayes Techniques
The rapid rate of innovations and dynamics of technology has made humans life more dependent on them. In today’s synopsis Microblogging and Social networking sites like Twitter, Facebook are a part of our lives that cannot be detached from anyone. Through these social media each one of them carry their emotions and fix their opinions based on a particular situations or circumstances. This paper presents a brief comparison about Detection and Classification of Emotions on Social Media using SVM and Näıve Bayesian classifier. Twitter messages has been used as input dataset because they contain a broad, varied, and freely accessible set of emotions. The approach uses hash-tags as labels to train supervised classifiers to detect multiple classes of emotion on potentially large data sets without the need for manual intervention. We look into the usefulness of a number of features for detecting emotions, including unigrams, unigram symbol, negations and punctuations using SVM and Näıve Bayesian Classifiers.