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Opinion Mining And Sentiment Analysis Techniques: A Recent Survey

Authors: Ms. Kalyani D. Gaikwad*, Prof. Sonawane V.R;

Opinion Mining And Sentiment Analysis Techniques: A Recent Survey

Abstract

Sentiment analysis (also known as opinion mining) refers to the use of natural language processing, text analysis and computational linguistics to identify and extract subjective information in source materials. Sentiment analysis is widely applied to reviews and social media for a variety of applications, ranging from marketing to customer service. The difficulties of performing sentiment analysis in this domain can be overcome by leveraging on common-sense knowledge bases. Opinion Mining is an area of text classification which continuously gives its contribution in research field. The main objective of Opinion mining is Sentiment Classification i.e. to classify the opinion into positive or negative classes. Further, most of the researchers implement the opinion mining by separating out the adverb-adjective combination present in the statements or classifying the verbs of statements. Opinion mining is the field of study related to analyze opinions, sentiments, evaluations, attitudes, and emotions of users which they express on social media and other online resources. RSS uses a family of standard web feed formats to publish frequently updated information: blog entries, news headlines.

Keywords

Opinion Mining, sentiment classification News Headlines analysis, SentiWordNet, Positive-Negative Scores.

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popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
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influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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