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DOI: 10.14569/IJACSA.2019.0100248
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A Study on Sentiment Analysis Techniques of Twitter Data

Author 1: Abdullah Alsaeedi
Author 2: Mohammad Zubair Khan

International Journal of Advanced Computer Science and Applications(IJACSA), Volume 10 Issue 2, 2019.

  • Abstract and Keywords
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Abstract: The entire world is transforming quickly under the present innovations. The Internet has become a basic requirement for everybody with the Web being utilized in every field. With the rapid increase in social network applications, people are using these platforms to voice them their opinions with regard to daily issues. Gathering and analyzing peoples’ reactions toward buying a product, public services, and so on are vital. Sentiment analysis (or opinion mining) is a common dialogue preparing task that aims to discover the sentiments behind opinions in texts on varying subjects. In recent years, researchers in the field of sentiment analysis have been concerned with analyzing opinions on different topics such as movies, commercial products, and daily societal issues. Twitter is an enormously popular microblog on which clients may voice their opinions. Opinion investigation of Twitter data is a field that has been given much attention over the last decade and involves dissecting “tweets” (comments) and the content of these expressions. As such, this paper explores the various sentiment analysis applied to Twitter data and their outcomes.

Keywords: Twitter; sentiment; Web data; text mining; SVM; Bayesian algorithm; hybrid; ensembles

Abdullah Alsaeedi and Mohammad Zubair Khan, “A Study on Sentiment Analysis Techniques of Twitter Data” International Journal of Advanced Computer Science and Applications(IJACSA), 10(2), 2019. http://dx.doi.org/10.14569/IJACSA.2019.0100248

@article{Alsaeedi2019,
title = {A Study on Sentiment Analysis Techniques of Twitter Data},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2019.0100248},
url = {http://dx.doi.org/10.14569/IJACSA.2019.0100248},
year = {2019},
publisher = {The Science and Information Organization},
volume = {10},
number = {2},
author = {Abdullah Alsaeedi and Mohammad Zubair Khan}
}



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.

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