New Approach to Sentiment Polarity Detection by using ML Techniques

Abstract

Sentiment analysis (SA) is a process of extracting the user’s feelings, emotions and verifying whether a user-generated text expresses neutral, positive or negative opinion about a product, people, topic or an event. The development of internet based applications has directed enormous measure of customized surveys for different related data on the Web. These reviews can be collected from various sources such as social media, social network, Wiki, forums, blogs, news and websites. As a result of the growing number of customer reviews, finding appropriate customer reviews will play important rule in reducing information overload. Sentiment Analysis is considered as one of the useful tool for users to extract the required data, as well as to aggregate the collective sentiments of the reviews. Because of rapid development of social media and Internet technologies, sentiment analysis has turned into an essential opinion mining technique. There are three noteworthy systems being utilized for sentiment analysis; Machine learning, dictionary based, and rule-based methodology. Each individual method is having some limitations. So in order to overcome these limitations in this paper we proposes an integrated framework which combines the above methods to achieve better scalability and accuracy.

Keywords: Sentiment Analysis, Machine Learning, Text Summarization, Review Analysis, Opinion Mining