Sentiment Analysis of Arabic Tweets using Deep Learning

Author:

Heikal Maha,Torki Marwan,El-Makky Nagwa

Publisher

Elsevier BV

Subject

General Engineering

Reference30 articles.

1. Abdul-Mageed, M., Diab, M., Korayem, M., (2011). “Subjectivity and sentiment analysis of modern standard Arabic”, in: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies: short papers-Volume 2, Association for Computational Linguistics. pp. 587–591.

2. SAMAR: Subjectivity and sentiment analysis for Arabic social media;Abdul-Mageed;Computer Speech & Language,2014

3. Al Sallab, A., Hajj, H., Badaro, G., Baly, R., El Hajj, W., Shaban, K.B., (2015). “Deep learning models for sentiment analysis in Arabic”, in: Proceedings of the Second Workshop on Arabic Natural Language Processing, pp. 9–17.

4. Baly, R., Badaro, G., El-Khoury, G., Moukalled, R., Aoun, R., Hajj, H., El-Hajj, W., Habash, N., Shaban, K., (2017)a. “A characterization study of Arabic twitter data with a benchmarking for state-of-the-art opinion mining models”, in: Proceedings of the Third Arabic Natural Language Processing Workshop, pp. 110–118.

5. Baly, R., Hajj, H., Habash, N., Shaban, K.B., El-Hajj, W., (2017)b. “A sentiment treebank and morphologically enriched recursive deep models for effective sentiment analysis in Arabic”. ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP) 16.

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