Sentiment analysis has emerged as a field that has attracted a significant amount of attention since it has a wide variety of applications that could benefit from its results, such as news analytics, marketing, question answering, knowledge management and so on. This area, however, is still early in its development where urgent improvements are required on many issues, particularly on the performance of sentiment classification. Understanding the thoughts of the people is an essential part of the information-gathering behavior. Opinion-rich resources like online review sites and personal blogs have gained immense popularity as they have become easily accessible and are giving new opportunities and posing new challenges as, now people actively use information technology to search out and understand the opinions of others. The flow of interest in the new systems that directly deals with the opinions as a first-class object has given rise to activities in the area of opinion mining and sentiment analysis that work towards the computational analysis of opinions, sentiments and subjectivity in the text. This paper reviews the sentiment analysis methodology and focuses on the techniques to deal with the challenges of sentiment-aware applications. The purpose of this paper is to describe sentiment analysis in detail and to illustrate the method used for it. This survey consists of approaches that work towards enabling opinion-oriented information seeking systems. The main contribution of this paper includes categorization of a number of articles over the years and the illustrations of the recent trends in research in sentiment analysis and its related areas.

Opinion mining; Sentiment analysis; Consumer attitude; Sentiment classification

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