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A Systematic Literature Review of Sentiment Analysis Techniques

J. Kaur1 , S.S. Sehra2 , S.K. Sehra3

Section:Review Paper, Product Type: Journal Paper
Volume-5 , Issue-4 , Page no. 22-28, Apr-2017

Online published on Apr 30, 2017

Copyright © J. Kaur, S.S. Sehra, S.K. Sehra . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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IEEE Style Citation: J. Kaur, S.S. Sehra, S.K. Sehra, “A Systematic Literature Review of Sentiment Analysis Techniques,” International Journal of Computer Sciences and Engineering, Vol.5, Issue.4, pp.22-28, 2017.

MLA Style Citation: J. Kaur, S.S. Sehra, S.K. Sehra "A Systematic Literature Review of Sentiment Analysis Techniques." International Journal of Computer Sciences and Engineering 5.4 (2017): 22-28.

APA Style Citation: J. Kaur, S.S. Sehra, S.K. Sehra, (2017). A Systematic Literature Review of Sentiment Analysis Techniques. International Journal of Computer Sciences and Engineering, 5(4), 22-28.

BibTex Style Citation:
@article{Kaur_2017,
author = {J. Kaur, S.S. Sehra, S.K. Sehra},
title = {A Systematic Literature Review of Sentiment Analysis Techniques},
journal = {International Journal of Computer Sciences and Engineering},
issue_date = {4 2017},
volume = {5},
Issue = {4},
month = {4},
year = {2017},
issn = {2347-2693},
pages = {22-28},
url = {https://www.ijcseonline.org/full_paper_view.php?paper_id=1235},
publisher = {IJCSE, Indore, INDIA},
}

RIS Style Citation:
TY - JOUR
UR - https://www.ijcseonline.org/full_paper_view.php?paper_id=1235
TI - A Systematic Literature Review of Sentiment Analysis Techniques
T2 - International Journal of Computer Sciences and Engineering
AU - J. Kaur, S.S. Sehra, S.K. Sehra
PY - 2017
DA - 2017/04/30
PB - IJCSE, Indore, INDIA
SP - 22-28
IS - 4
VL - 5
SN - 2347-2693
ER -

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Abstract

Development of Web 2.0 has resulted in enormous increase in the vast source of opinionated user generated data. Sentiment Analysis includes extracting, grasping, arranging and presenting the feelings or suppositions communicated in the information gathered from the clients. This paper exhibits an efficient writing survey of different strategies of sentiment analysis. A model for sentiment analysis of twitter data using existing techniques is constructed for comparative analysis of various approaches. Dataset is pre-processed for noise removal and unigrams as well as bigrams are used for feature extraction with term frequency as weighting criteria. Maximum accuracy is achieved by using a combination of SVM and Naïve Bayes at 78.60% employing unigrams and 81.40% employing bigrams as features.

Key-Words / Index Term

Sentiment Analysis, Crowdsourced data, Twitter, Machine Learning Techniques

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