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Survey of Techniques for Opinion Mining

by Nilesh M. Shelke, Shriniwas Deshpande, Vilas Thakre
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 57 - Number 13
Year of Publication: 2012
Authors: Nilesh M. Shelke, Shriniwas Deshpande, Vilas Thakre
10.5120/9176-3579

Nilesh M. Shelke, Shriniwas Deshpande, Vilas Thakre . Survey of Techniques for Opinion Mining. International Journal of Computer Applications. 57, 13 ( November 2012), 30-35. DOI=10.5120/9176-3579

@article{ 10.5120/9176-3579,
author = { Nilesh M. Shelke, Shriniwas Deshpande, Vilas Thakre },
title = { Survey of Techniques for Opinion Mining },
journal = { International Journal of Computer Applications },
issue_date = { November 2012 },
volume = { 57 },
number = { 13 },
month = { November },
year = { 2012 },
issn = { 0975-8887 },
pages = { 30-35 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume57/number13/9176-3579/ },
doi = { 10.5120/9176-3579 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:00:22.437366+05:30
%A Nilesh M. Shelke
%A Shriniwas Deshpande
%A Vilas Thakre
%T Survey of Techniques for Opinion Mining
%J International Journal of Computer Applications
%@ 0975-8887
%V 57
%N 13
%P 30-35
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Opinion mining refers to computational techniques for analyzing the opinions that are extracted from various sources. Existing research work on Opinion is based upon business and e-commerce such as product reviews and movie ratings. Opinion mining involves computational treatment of opinion and subjectivity in text. It has suddenly attracted the attention of the researcher fraternity. This survey paper describes techniques and approaches that promise to directly enable opinion-oriented information seeking systems. An attempt has been made to discuss in de tails various approaches to perform a computational treatment of sentiments and opinions. Various supervised or data-driven techniques for opinion mining like Naïve Byes, Maximum Entropy, SVM are discussed and their strengths and drawbacks are touched upon.

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Index Terms

Computer Science
Information Sciences

Keywords

Polar expression opinion mining POS tagger entropy corpus sentiment emotion machine learning

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