International Journal of Scientific & Technology Research

Home About Us Scope Editorial Board Blog/Latest News Contact Us
10th percentile
Powered by  Scopus
Scopus coverage:
Nov 2018 to May 2020


IJSTR >> Volume 9 - Issue 8, August 2020 Edition

International Journal of Scientific & Technology Research  
International Journal of Scientific & Technology Research

Website: http://www.ijstr.org

ISSN 2277-8616

Analysis Of Satisfaction Of Banque Populaire Customers Through Their Tweets

[Full Text]



Youssef CHOUNI, Mohammed ERRITALI, Youssef OUADID



Social networks, Twitter, Sentiment analysis(SA), WorldNet, language R, Machine learning, SVM, Naïve Bayes(NB).



The Social networks are an excellent source of information, and extraction of opinion. Nowadays, the most of internet users are using these platforms in order to share their sentiments and opinions about the products or services. The exploitation of these opinions is fruitfully. In this work, we expose the problem of sentiment analysis in social networks by showing the multiple experiments made in this context on Tweets using the two important approaches of this domain, namely, the Lexicon-Based Approach and the Machine Learning Approach. Also we introduce an original approach which incorporates the semantics in the second approach using the WorldNet lexical database.



[1] Eugenio Martínez-Cámara & all “Polarity classification for Spanish Tweets using the COST corpus” Journal of Information Science, 2014, pp. 1-11 © The Author(s), DOI: 10.1177/0165551510000000.
[2] Richa Mathur & all “Analyzing Sentiment of Twitter Data using Machine Learning Algorithm” GADL Journal of Inventions in Computer Science and Communication Technology (JICSCT) Volume-4, Issue-2 2018.
[3] Abdeljalil EL ABDOULI & all “Sentiment Analysis of Moroccan Tweets using Naïve Bayes Algorithm” International Journal of Computer Science and Information Security (IJCSIS),Vol. 15, No. 12, December 2017.
[4] Arun Manivannan & all “Hybrid classifier for analyzing Twitter Sentiment”
[5] Karthika K. “An efficient approach for sarcasm detection in tweets using polarity flip” International Journal of Advance Research, Ideas and Innovations in Technology ISSN: 2454-132X Impact factor: 4.295 (Volume 5, Issue 2) 2019.
[6] Taboada & all “Lexicon-Based Methods for Sentiment Analysis” Computational linguistics 37, no. 2 (2011).
[7] Amit Agarwal & all “Application of Lexicon Based Approach in Sentiment Analysis for short Tweets” International Conference on Advances in Computing and Communication Engineering (ICACCE-2018).
[8] Yassine Al-Amrani & all “Sentiment Analysis using supervised classification algorithms” The 2nd international Conference on Big Data, Cloud and Applications 2017.
[9] Rahim Dehkharghani “Building Phrase Polarity Lexicons for Sentiment Analysis” International Journal of Interactive Multimedia and Artificial Intelligence 2018.
[10] D. Davidov & all "Enhanced sentiment learning using twitter hashtags and smileys", Proceedings of Coling, 2010