Sentiment Analysis of Arabic Tweets about Violence Against Women using Machine Learning

Authors

  • Hassan Najadat, Dr. Department of Computer Information Systems Jordan University of Science and Technology, Irbid, Jordan
  • Moath Mohmmad Zyout Department of Computer Information Systems Jordan University of Science and Technology, Irbid, Jordan
  • Emran AL Bashabsheh Department of Computer Information Systems Jordan University of Science and Technology, Irbid, Jordan

Keywords:

Sentiment analysis, Support Vector Machines, Na¨ıve Bayes, K-nearest-neighbor, Deep Learning, Decision Trees, SA, SVM, NB, KNN, DL, DT

Abstract

Social Media platforms, such as Twitter become a significant pulse of smart societies that are shaping our communities by sensing people’s information and perceptions across living areas over space and time. Social media sentiment analysis helps in recognizing people’s emotions and attitudes and assessing various public issues, for example, women’s rights and violence against women. In this paper, we use the sentence based sentiment analysis to study the topic of women’s rights. We collected dialect Arabic tweets from the whole Arab world as data via a Twitter API, and we clean it to use in the classification step. We have examined different types of traditional classification algorithms namely, Support Vector Machine, K-Nearest-Neighbour, Decision Trees, and Naıve Bayes, then compared these results with deep learning results. Finally, we compared the classification results using the precision, recall, and accuracy measurements and we found the Support Vector Machine algorithm gains the best results, while the Naıve Baye was the worst. We also noticed that there is increasing attention to women’s rights in the Arab world

References

S. Alotaibi, R. Mehmood, and I. Katib, “Sentiment analysis of Arabic tweets in smart cities: A review of saudi dialect,” in 2019 Fourth International Conference on Fog and Mobile Edge Computing (FMEC). IEEE, 2019, pp. 330–335.

Twitter website: https://twitter.com.

number of Twitter users.[Online]. Available: https://www.statista.com/statistics/242606/number-of-active-twitter-users-in-selected-countries/.

K. M. Alomari, H. M. ElSherif, and K. Shaalan, “Arabic tweets sentimental analysis using machine learning,” in International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems. Springer, 2017, pp. 602–610.

M. Biltawi, W. Etaiwi, S. Tedmori, A. Hudaib, and A. Awajan, “Sentiment classification techniques for arabic language: A survey,” in 2016 7th International Conference on Information and Communication Systems (ICICS). IEEE, 2016, pp. 339–346.

A. Farghaly and K. Shaalan, “Arabic natural language processing: Challenges and solutions,” ACM Transactions on Asian Language Information Processing (TALIP), vol. 8, no. 4, p. 14, 2009.

N. K. Laskari and S. K. Sanampudi, “Aspect based sentiment analysis survey,” IOSR Journal of Computer Engineering (IOSR-JCE), vol. 18, no. 2, pp.

–28, 2016.

T. A. Rana and Y.-N. Cheah, “Aspect extraction in sentiment analysis: comparative analysis and survey,” Artificial Intelligence Review, vol. 46, no. 4, pp. 459–483, 2016.

M. Al-Ayyoub, S. B. Essa, and I. Alsmadi, “Lexicon-based sentiment analysis of arabic tweets.” IJSNM, vol. 2, no. 2, pp. 101–114, 2015.

H. Saif, Y. He, M. Fernandez, and H. Alani, “Contextual semantics for sentiment analysis of twitter,” Information Processing & Management, vol. 52, no. 1, pp. 5–19, 2016.

G. Alwakid, T. Osman, and T. Hughes-Roberts, “Challenges in sentiment analysis for arabic social networks,” Procedia Computer Science, vol. 117, pp.

–100, 2017.

M. Ghiassi, J. Skinner, and D. Zimbra, “Twitter brand sentiment analysis: A hybrid system using n-gram analysis and dynamic artificial neural network,”

Expert Systems with applications, vol. 40, no. 16, pp. 6266–6282, 2013.

R. M. Duwairi, “Sentiment analysis for dialectical arabic,” in 2015 6th International Conference on Information and Communication Systems (ICICS).

IEEE, 2015, pp. 166–170.

A. Go, R. Bhayani, and L. Huang, “Twitter sentiment classification using distant supervision,” CS224N Project Report, Stanford, vol. 1, no. 12, p. 2009, 2009.

M. Heikal, M. Torki, and N. El-Makky, “Sentiment analysis of arabic tweets using deep learning,” Procedia Computer Science, vol. 142, pp. 114–122, 2018.

R. M. Duwairi and I. Qarqaz, “Arabic sentiment analysis using supervised classification,” in 2014 International Conference on Future Internet of Things and Cloud. IEEE, 2014, pp. 579–583.

rapidminer website.[Online]. Available: https://rapidminer.com/.

tableau website.[Online]. Available: https://www.tableau.com/.

A. Agarwal, B. Xie, I. Vovsha, O. Rambow, and R. Passonneau, “Sentiment analysis of twitter data,” in Proceedings of the Workshop on Language in Social Media (LSM 2011), 2011, pp. 30–38.

M. Thelwall, K. Buckley, and G. Paltoglou, “Sentiment in twitter events,” Journal of the American Society for Information Science and Technology, vol. 62, no. 2, pp. 406–418, 2011.

E. Kontopoulos, C. Berberidis, T. Dergiades, and N. Bassiliades, “Ontology-based sentiment analysis of twitter posts,” Expert systems with applications, vol. 40, no. 10, pp. 4065–4074, 2013.

N. Al-Twairesh, H. Al-Khalifa, A. Alsalman, and Y. Al-Ohali, “Sentiment analysis of arabic tweets: Feature engineering and a hybrid approach,” arXiv preprint arXiv:1805.08533, 2018.

Z. Jianqiang and G. Xiaolin, “Comparison research on text pre-processing methods on twitter sentiment analysis,” IEEE Access, vol. 5, pp. 2870–2879, 2017.

D. Tang, F. Wei, N. Yang, M. Zhou, T. Liu, and B. Qin, “Learning sentiment-specific word embedding for twitter sentiment classification,” in Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 2014, pp. 1555–1565.

L. Al-Horaibi and M. B. Khan, “Sentiment analysis of arabic tweets using semantic resources,” International Journal of Computing & Information Sciences, vol. 12, no. 2, p. 149, 2016.

A. Mittal and A. Goel, “Stock prediction using twitter sentiment analysis,” Standford University, CS229 (2011 http://cs229. stanford. edu/proj2011/GoelMittal-StockMarketPredictionUsingTwitterSentimentAnalysis. pdf), vol. 15, 2012.

A. Tumasjan, T. O. Sprenger, P. G. Sandner, and I. M. Welpe, “Predicting elections with twitter: What 140 characters reveal about political sentiment,” in Fourth international AAAI conference on weblogs and social media, 2010.

A. Hasan, S. Moin, A. Karim, and S. Shamshirband, “Machine learning-based sentiment analysis for twitter accounts,” Mathematical and Computational Applications, vol. 23, no. 1, p. 11, 2018.

O. Araque, I. Corcuera-Platas, J. F. S a´nchez-Rada, and C. A. Iglesias, “Enhancing deep learning sentiment analysis with ensemble techniques in social applications,” Expert Systems with Applications, vol. 77, pp. 236–246, 2017.

I. Chaturvedi, E. Cambria, R. E. Welsch, and F. Herrera, “Distinguishing between facts and opinions for sentiment analysis: Survey and challenges,”

Information Fusion, vol. 44, pp. 65–77, 2018.

M. Al-Smadi, O. Qawasmeh, M. Al-Ayyoub, Y. Jararweh, and B. Gupta, “Deep recurrent neural network vs. support vector machine for aspect-based sentiment analysis of arabic hotels’ reviews,” Journal of computational science, vol. 27, pp. 386–393, 2018.

M. Al-Smadi, B. Talafha, M. Al-Ayyoub, and Y. Jararweh, “Using long short-term memory deep neural networks for aspect-based sentiment analysis of arabic reviews,” International Journal of Machine Learning and Cybernetics, vol. 10, no. 8, pp. 2163–2175, 2019.

K. Abu Kwaik, M. K. Saad, S. Chatzikyriakidis, and S. Dobnik, “Lstm-cnn deep learning model for sentiment analysis of dialectal arabic,” LSTM-CNN Deep Learning Model for Sentiment Analysis of Dialectal Arabic, vol. 1108, 2019.

A. Mohammed and R. Kora, “Deep learning approaches for arabic sentiment analysis,” Social Network Analysis and Mining, vol. 9, no. 1, p. 52, 2019.

A. Alharbi, M. Taileb, and M. Kalkatawi, “Deep learning in arabic sentiment analysis: An overview,” Journal of Information Science, p. 0165551519865488, 2019

Published

2021-10-15

How to Cite

Najadat, H., Zyout, M. M., & AL Bashabsheh, E. (2021). Sentiment Analysis of Arabic Tweets about Violence Against Women using Machine Learning. Indonesian Journal of Computer Science, 10(2). Retrieved from http://ijcs.stmikindonesia.ac.id/index.php/ijcs/article/view/324

Issue

Section

English Articles