Sentiment classification on big data using Naïve bayes and logistic regression
Anjuman Prabhat, Vikas Khullar · 2017
The huge expansion of world wide web has involved a contemporary fashion of conveying the attitude or viewpoint of human being. It is a channel where anybody any visualize opinion and sentiments of different customers. It is also possible to see opinion classified into different categories and ratings given on different products. This information plays a supreme role in sentiment classification task. The huge amount of data stored online can be mined effectively to extract valuable information and do decision based on extracted information. The real time Twitter reviews are feed to different supervised machine learning classifier. After training the classification is carried out by various classifiers. The tweets as categorized as positive or, negative. In this paper we have used Naïve Bayes and Logistic Regression for the classification of Twitters reviews. The performance of algorithms has been evaluated on the basis of different parameter like accuracy, precision and throughput.