Sentiment Analysis Survey

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A Survey: Sentiment Analysis and Opinion Mining Shailesh Kumar Yadav Department of Computer Science, Pondicherry University, India pu.shailesh@gmail.com Abstract— Sentiment Analysis (SA) or opinion mining (OM) has recently become the focus of many researchers, because analysis of online text is beneficial and demanded for market research, scientific surveys from psychological and sociological perspective, political polls, business intelligence, enhancement of online shopping infrastructures, etc. Nowadays if one wants to buy a consumer product one prefer user reviews and discussion in public forums on web about the product. As a result opinion mining has gained importance. This online word-of-mouth represent new and measurable source…show more content…
It represents a large problem space. There are also many names and slightly different tasks, e.g. sentiment analysis, opinion mining, opinion extraction, sentiment mining, subjectivity analysis, affect analysis, emotion analysis, review mining, etc. However, they are now all under the umbrella of sentiment analysis or opinion mining. While in industry, the term sentiment analysis is more commonly used, but in academia both sentiment analysis and opinion mining are frequently employed. They basically represent the same field of study. The term sentiment analysis perhaps first used by Nasukawa and Yi in 2003[1], and the term opinion mining first used by Dave, Lawrence…show more content…
Therefore, sentiment analysis is an opportunity for NLP researchers to make tangible progress on all fronts of NLP, and potentially have a huge practical impact. II. LITERATURE SURVEY In [1], Nasukawa and Yi. illustrate a sentiment analysis approach to extract sentiments associated with polarities of positive or negative for specific subjects from a document, instead of classifying the whole document into positive or negative. The essential issues in sentiment analysis are to identify how sentiments are expressed in texts and whether the expressions indicate positive (favorable) or negative (unfavorable) opinions toward the subject. Powerful functionality for these kinds of issues is used. In [3], Ding et al. proposed an effective method for identifying semantic orientations of opinions expressed by reviewers on product features. It is able to deal with two major problems with the existing methods, (1) opinion words whose semantic orientations are context dependent, and (2) aggregating multiple opinion words in the same sentence. For (1), a holistic approach is proposed that can accurately infer the semantic orientation of an opinion word based on the review context. For (2), a new function to combine multiple opinion words in the same sentence is

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