Twitter benchmark dataset for arabic sentiment analysis
Автор: Donia Gamal, Marco Alfonse, El-Sayed M. El-Horbaty, Abdel-Badeeh M.Salem
Журнал: International Journal of Modern Education and Computer Science @ijmecs
Статья в выпуске: 1 vol.11, 2019 года.
Бесплатный доступ
Sentiment classification is the most rising research areas of sentiment analysis and text mining, especially with the massive amount of opinions available on social media. Recent results and efforts have demonstrated that there is no single strategy can mutually accomplish the best prediction performance on various datasets. There is a lack of existing researches to Arabic sentiment analysis compared to English sentiment analysis, because of the unique nature and difficulty of the Arabic language which leads to shortage in Arabic dataset used in sentiment analysis. An Arabic benchmark dataset is proposed in this paper for sentiment analysis showing the gathering methodology of the most recent tweets in different Arabic dialects. This dataset includes more than 151,000 different opinions in variant Arabic dialects which labeled into two balanced classes, namely, positive and negative. Different machine learning algorithms are applied on this dataset including the ridge regression which gives the highest accuracy of 99.90%.
Arabic Dialects, Arabic Sentiment Analysis, Arabic Opinion Mining, Twitter, Arabic Benchmark Dataset, Machine Learning
Короткий адрес: https://sciup.org/15016822
IDR: 15016822 | DOI: 10.5815/ijmecs.2019.01.04
Список литературы Twitter benchmark dataset for arabic sentiment analysis
- Uysal, Alper Kursat, and Yi Lu Murphey. "Sentiment classification: Feature selection based approaches versus deep learning.", Proceedings of IEEE International Conference Computer and Information Technology (CIT), pp. 23-30. IEEE, 2017.
- Abdul-Mageed Muhammad, and Mona T. Diab., AWATIF: A Multi-Genre corpus for modern standard Arabic subjectivity and sentiment analysis, Proceedings of the Eight International Conference on Language Resources and Evaluation (LREC’12), European Language Resources Association (ELRA), pp. 3907-3914, 2012.
- Medhat Walaa, Ahmed Hassan, and Hoda Korashy, Sentiment analysis algorithms and applications: A survey, Ain Shams Engineering Journal 5, no. 4, pp. 1093-1113, 2014.
- Korayem Mohammed, David Crandall, and Muhammad Abdul-Mageed, Subjectivity and sentiment analysis of arabic: A survey, Proceedings of International conference on advanced machine learning technologies and applications, pp. 128-139. Springer, Berlin, Heidelberg, 2012.
- Dehkharghani, Rahim, Berrin Yanikoglu, Yucel Saygin, and Kemal Oflazer. "Sentiment analysis in Turkish at different granularity levels.", International Journal of Natural Language Engineering, vol. 23, no. 4, pp. 535-559, 2017.
- Märkle-Huß, Joscha, Stefan Feuerriegel, and Helmut Prendinger. "Improving sentiment analysis with document-level semantic relationships from rhetoric discourse structures." In Proceedings of the 50th Hawaii International Conference on System Sciences, pp. 1142-1151. HICSS, 2017.
- Pudaruth, Sameerchand, Sharmila Moheeputh, Narmeen Permessur, and Adeelah Chamroo. "Sentiment Analysis from Facebook Comments using Automatic Coding in NVivo 11.", International Journal of Advances in Distributed Computing and Artificial Intelligence Journal (ADCAIJ), vol. 7, no. 1,pp. 41-48, 2018.
- Trupthi, M., Suresh Pabboju, and G. Narasimha. "Sentiment analysis on twitter using streaming API." Proceedings of IEEE 7th International In Advance Computing Conference (IACC), pp. 915-919. IEEE, 2017.
- Elhawary Mohamed, and Mohamed Elfeky, Mining Arabic business reviews, Proceedings of the IEEE International Conference on Data Mining Workshops, IEEE Computer Society, pp. 1108-1113, 2010.
- Roesslein, Joshua. "tweepy Documentation", http://docs.tweepy.org/en/v3.5.0/, 2009 [last accessed July 2018]
- https://www.arabacademy.com[last accessed July 2018]
- Liu Bing. Sentiment analysis and opinion mining. Synthesis lectures on human language technologies 5, no. 1 pp. 1-167, 2012.
- Ahmed Soha, Michel Pasquier, and Ghassan Qadah, Key issues in conducting sentiment analysis on Arabic social media text, Proceedings of 9th International Conference on Innovations in Information Technology (IIT), pp. 72-77. IEEE, 2013.
- Aly Mohamed, and Amir Atiya. Labr: A large scale arabic book reviews dataset. In Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics , vol. 2, pp. 494-498. 2013.
- Seiffert Chris, Taghi M. Khoshgoftaar, Jason Van Hulse, and Amri Napolitano, Building Useful Models from Imbalanced Data with Sampling and Boosting, In Proceedings of Florida Artificial Intelligence Research Society (FLAIRS) conference, pp. 306-311. 2008.
- Wawre Suchita V., and Sachin N. Deshmukh, Sentiment classification using machine learning techniques, International Journal of Science and Research (IJSR) 5, no. 4, pp. 819-821, 2016
- Zhao Jun, Kang Liu, and Liheng Xu, Sentiment analysis: mining opinions, sentiments, and emotions, International Journal of Computational Linguistics, Vol. 42, No. 3, pp. 595-598, 2016.
- Pozzi Federico Alberto, Elisabetta Fersini, Enza Messina, and Bing Liu. Sentiment analysis in social networks. Morgan Kaufmann, 2016.
- https://www.nltk.org/ [last access July 2018]
- http://scikit-learn.org/ [last access July 2018]
- Pang Bo, Lillian Lee, and Shivakumar Vaithyanathan. Thumbs up?: sentiment classification using machine learning techniques. In Proceedings of the Association for Computational Linguistics (ACL-02) conference on Empirical methods in natural language processing, Vol. 10, pp. 79-86. 2002.
- Kouloumpis Efthymios, Theresa Wilson, and Johanna D. Moore, Twitter sentiment analysis: The good the bad and the omg!, In Proceedings of the 12th International AAAI Conference on Web and Social Media (ICWSM 11), no. 538-541, pp. 538-541, 2011.
