Opinion Mining on Twitter Data Using Supervised Machine Learning Algorithms - Mathews, Deepa Mary; Abraham, Sajimon (International Journal of Computer Sciences and Engineering, 2018)
The emerging digital era generates heaps of computerized information. The greater part of the electronic data in the world today has been created over the last recent couple of years. The velocity of data generation is unimaginable and incomprehensible. People nowadays are commonly using the digital media to express their stand point about a topic. These opinions are analyzed automatically to know whether the client remark is ideal or not good to the said theme. This ought to be possible by Opinion Mining, also called as Sentiment Analysis. The basic chore in Sentiment Analysis is to categorize the orientation of a given review and subsequently identifying whether the sentiment implied is positive, negative or fair. In this paper, the tweets based on the news thread “Whether National Anthem is needed at Cinema theatres?” are analyzed based on the user rating for the opinions. The classifiers like Bernoulli and Multinomial Naive Bayes, Random Forest, k-Nearest Neighbor (k-NN) and Support Vector Machine (SVM) have been used for analyzing the opinions and found that the Random Forest classifier and Multinomial Naïve Bayes classifier is the top rated classifier based on their accuracy values.