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Sentiment Analysis Methods

Abstract

As more and more information becomes freely available on the Internet, there appears to be an increasing demand for tools that would analyze information and provide valuable insights. One of such tools is sentiment analysis. The goal of sentiment analysis is to extract an opinion from text data and classify it into positive, neutral or negative. Millions of opinions, which are expressed on the Internet, about a product can be analyzed in a matter of seconds, providing insights about the appealing of the product to the general public based on the ration of positive to negative feedbacks. It is not feasible to complete such classification manually until, so the help of computer aid is needed. The goal of this paper is to overview available methods and algorithms that are commonly used for solving the problem of sentiment analysis. A basic machine learning algorithm has been built based on Naïve Classifier and Bag of Word model. The results was satisfying with a relatively height accuracy of classification. Continue reading Sentiment Analysis Methods