Twitter Sentimental Analysis Using Augmented Naive-BayesAlgorithm

Abstract

Sentimental analysis is used in text mining. Twitter is one of the prominentsocial media. Twitter offers organizations a quick and active way to analyze customerviewpoints toward the critical to success within the marketplace. Natural Language Processing, algorithms like the Support vector machine, Naive Bayes is employed to predict the polarity of a sentence. Sentiment scrutinity of Twitter data may be classified upon sentence and document level. The outcomes classify customer perspective via tweets into positive negative and neutral comments, which is represented in a pie chart. This method mostly used in the Market Analysis to the prediction about a product or review about a product. In our proposed method the Naive Bayes algorithm is used for effectiveness and faster processing.