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Article Details

Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

Sentiment Analysis using SVM: A Systematic Literature Review

Author 1: Munir Ahmad
Author 2: Shabib Aftab
Author 3: Muhammad Salman Bashir
Author 4: Noureen Hameed

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Digital Object Identifier (DOI) : 10.14569/IJACSA.2018.090226

Article Published in International Journal of Advanced Computer Science and Applications(IJACSA), Volume 9 Issue 2, 2018.

  • Abstract and Keywords
  • How to Cite this Article
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Abstract: The world has revolutionized and phased into a new era, an era which upholds the true essence of technology and digitalization. As the market has evolved at a staggering scale, it is must to exploit and inherit the advantages and opportunities, it provides. With the advent of web 2.0, considering the scalability and unbounded reach that it provides, it is detrimental for an organization to not to adopt the new techniques in the competitive stakes that this emerging virtual world has set along with its advantages. The transformed and highly intelligent data mining approaches now allow organizations to collect, categorize, and analyze users’ reviews and comments from micro-blogging sites regarding their services and products. This type of analysis makes those organizations capable to assess, what the consumers want, what they disapprove of, and what measures can be taken to sustain and improve the performance of products and services. This study focuses on critical analysis of the literature from year 2012 to 2017 on sentiment analysis by using SVM (support vector machine). SVM is one of the widely used supervised machine learning techniques for text classification. This systematic review will serve the scholars and researchers to analyze the latest work of sentiment analysis with SVM as well as provide them a baseline for future trends and comparisons.

Keywords: Sentiment analysis; polarity detection; machine learning; SVM; support vector machine; SLR; systematic literature review

Munir Ahmad, Shabib Aftab, Muhammad Salman Bashir and Noureen Hameed, “Sentiment Analysis using SVM: A Systematic Literature Review” International Journal of Advanced Computer Science and Applications(IJACSA), 9(2), 2018. http://dx.doi.org/10.14569/IJACSA.2018.090226

@article{Ahmad2018,
title = {Sentiment Analysis using SVM: A Systematic Literature Review},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2018.090226},
url = {http://dx.doi.org/10.14569/IJACSA.2018.090226},
year = {2018},
publisher = {The Science and Information Organization},
volume = {9},
number = {2},
author = {Munir Ahmad and Shabib Aftab and Muhammad Salman Bashir and Noureen Hameed}
}


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