ABSTRACT
Textual data available online in the form of tweets, posts, messages, blogs, reviews, comments etc. have proven to be instrumental in comprehending the sentimental needs and necessities of an individual. With the increase in the number of users joining the social communities, there has been a whopping demand among researchers to analyze the online content by classifying text to predict sentiments and emotional traits as much consummately as possible. In this paper, we have proposed our text classification algorithms based on the Machine Learning approaches and the other on Lexicon-Based approach to observe how they work. Further, we have specified the performance-wise differences between the three classifiers viz. SVM classifier, Naïve Bayes Classifier and Dictionary-Based classifier to prove the supremacy of one over the other.
