A Comparative Classification of Approaches and Applications in Opinion Mining

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Razieh Asgarnezhad and Keyvan Mohebbi

Abstract

With the growing availability of online resources on Web and popularity of fast and rich resources of opinion sharing such as online review sites and personal blogs, opinion mining has become an interesting area of research. Opinion mining is a process that is used for automatic extraction of knowledge from the opinion of others about some particular topic or problem. In addition, sentiment analysis, an application of natural language processing, has been witnessed a blooming interest over the past decades. Sentiment analysis is an extension of data mining that extracts and analyzes the unstructured data automatically. The aim of sentiment analysis and opinion mining is extraction of opinion from Web sites and classifying the polarity of text in terms of positive (good), negative or neutral (surprise). Mood mining causes make-decisions to be done automatically. The purpose of this study is to illustrate of the recent trend of research in the sentiment analysis and its related areas. In this paper, we survey various techniques of sentiment analysis and propose a new classification of these techniques. In the end, we present a comparative evaluation of such techniques in terms of accuracy, f-measure, and f-score

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