A Comparative Analysis of Sentiment Classification using Machine Learning Algorithms

G. Prathap and Dr.R. Rathinasabapathy

Sentimental analysis is the development of identifying the human’s thoughts or feelings. So Numerous approaches have been created for the sentimental analysis. Sentiment analysis is a progression that automates mining of attitudes, opinions, views and emotions from text, speech, tweets and database sources. It fascinates many researchers which could support many real-world applications. Most of the traditional methods are prominently utilizes the standard feeling thesaurus and statistical methods. These approaches are not scalable to the social media big data. Machine learning is one of the expansively utilized strategies in the direction of sentiment classification. Here in this paper a comparative analysis of three of the supervised Machine learning Algorithms, KNN Classifier, Naïve Bayes Classifier is performed. From the results it is assured that SVM performs better than the other two classifiers in terms of accuracy, Precision and Recall.

Volume 11 | 04-Special Issue

Pages: 1681-1688