A Comparative Study of Detection and Classification of Emotions on Social Media Using SVM and Näıve Bayes Techniques
Aysha A, Syed Meeral MK, Bushra KM · Advances in parallel computing · 2021
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.