Utilizing machine learning in Sentiment Analysis: SentiRobo approach

Abstract

Following the rapid evolution of Web 2.0, Sentiment Analysis has become one of the major techniques for mining the social media content. It aims to analyze opinions, sentiments, attitudes, and emotions towards entities such as topics, products, organizations, individuals, communities, and services. This paper presents SentiRobo, a supervised machine learning approach for the process of Sentiment Analysis. An enhanced version of Naive Bayes algorithm is introduced to predict the sentiment polarity of social media large data sets. Empirical evaluation over different twitter datasets with more than 300,000 records reveals the merit of this approach in processing of social media datasets.