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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.

Mining Trending Hash Tags for Arabic Sentiment Analysis

Author 1: Yahya AlMurtadha

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Digital Object Identifier (DOI) : 10.14569/IJACSA.2018.090227

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

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Abstract: People text millions of posts everyday on microblogging social networking especially Twitter which make microblogs a rich source for public opinions, customer’s comments and reviews. Companies and public sectors are looking for a way to measure the public response and feedback on particular service or product. Sentiment analysis is an encouraging technique capable to sense the public opinion in a fast and less cost tactic than traditional survey methods like questionnaires and interviews. Various sentiment methods were developed in many languages, such as English and Arabic with much more studies in the first one. Sometime, hash tags are misleading or may have a title that does not really reflects the subject. Tweets in trend hash tags may contain keyword or topics titles better represent the subject of the hash tag. This research aims at proposing an approach to explore Twitter Hash tag trends to retrieve tweets, group retrieved tweets to learn topics’ profiles, do sentiment analysis to test the subjectivity of tweets then develop a prediction model using deep learning to classify a new tweet to the appropriate topic profile. Arabic hash tags trends have been used to evaluate the proposed approach. The performance of the proposed approach (clustering topics within hashtag trend to learn topics profiles then do sentiment analysis) shows better accuracy than sentiment analysis without clustering the topics.

Keywords: Arabic sentiment analysis; twitter; opinion mining; trending hashtags; text analysis; deep learning

Yahya AlMurtadha, “Mining Trending Hash Tags for Arabic Sentiment Analysis” International Journal of Advanced Computer Science and Applications(IJACSA), 9(2), 2018. http://dx.doi.org/10.14569/IJACSA.2018.090227

@article{AlMurtadha2018,
title = {Mining Trending Hash Tags for Arabic Sentiment Analysis},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2018.090227},
url = {http://dx.doi.org/10.14569/IJACSA.2018.090227},
year = {2018},
publisher = {The Science and Information Organization},
volume = {9},
number = {2},
author = {Yahya AlMurtadha}
}


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