Deep Neural Network for Arabic Tweets Sentiment Analysis Related to COVD-19
Keywords:Arabic Text, Deep Learning, Machine Learning, Opinion mining, NLP, Covid 19, Coronavirus, Naïve Bayes, Sentiment Analysis;, LSTM, CNN
Aside from the Coronavirus pandemic, several other major crises erupted worldwide. Various industries have been irreparably harmed, and many organizations have succumbed to the calamity. There is an unavoidable need to examine various patterns on social media platforms in order to reduce public anxiety and misconceptions. The study examines the emotional orientations of Arabic persons who use social media, specifically the Twitter platform. From November 2020 to January 2021, we gathered data from Twitter. Tweets have been sent from a number of Arab cities. Natural Language Processing (NLP) and Machine Learning (ML) methods are used to identify whether an opinion's sentiment is favorable, negative, or neutral. This study gathers Arabic tweets and then manually annotates them to classify them as negative, positive, neutral, etc. In this work, word embedding and TFIDF are employed as vector features, with Long Short-Term Memory (LSTM) and Naive Bayes used for classification. This paper offers a learned LSTM model and a Naive Bayes model based on the collected tweets, leveraging two powerful ML methods. The LSTM model is more effective than the Nave Bayes model since its superior performance. This is noted by achieving an accuracy of 99% for the LSTM model. The analysis of this study aids various governments and corporate entities in better understanding public attitude and behavior in the face of the pandemic and making strategic decisions in response. Furthermore, this study focuses on data visualization by exhibiting an emotion plot and a word cloud. This study is an extended version of our work "the Sentiment Analysis of Arabic Tweets Related to COVID-19 Using Deep Neural Network".
. Lipton, Z. C., Berkowitz, J., & Elkan, C. (2015). A critical review of recurrent neural networks for sequence learning. arXiv preprint arXiv:1506.00019.
. CDC, available online at: https://www.cdc.gov/coronavirus/2019-ncov/symptoms-testing/symptoms.html; accessed October 2020.
. World Meter, [available online at: https://www.worldometers.info/coronavirus/; accessed October 2020.
. Abirami, A. M., & Gayathri, V. (2017, January). A survey on sentiment analysis methods and approach. In 2016 Eighth International Conference on Advanced Computing (ICoAC) (pp. 72-76). IEEE.
. Shu, K., Sliva, A., Wang, S., Tang, J., & Liu, H. (2017). Fake news detection on social media: A data mining perspective. ACM SIGKDD explorations newsletter, 19(1), 22-36.
. Makris, C., Pispirigos, G., & Rizos, I. O. (2020). A distributed bagging ensemble methodology for community prediction in social networks. Information, 11(4), 199.
. Heist, N., Hertling, S., & Paulheim, H. (2018). Language-agnostic relation extraction from abstracts in Wikis. Information, 9(4), 75.
. Kretinin, A., Samuel, J., & Kashyap, R. (2018). When the going gets tough, the tweets get going! An exploratory analysis of tweets sentiments in the stock market. American Journal of Management, 18(5), 23-36.
. De Choudhury, M., Counts, S., & Horvitz, E. (2013, April). Predicting postpartum changes in emotion and behavior via social media. In Proceedings of the SIGCHI conference on human factors in computing systems (pp. 3267-3276).
. Wang, Z., Ye, X., & Tsou, M. H. (2016). Spatial, temporal, and content analysis of Twitter for wildfire hazards. Natural Hazards, 83(1), 523-540.
. Skoric, M. M., Liu, J., & Jaidka, K. (2020). Electoral and Public Opinion Forecasts with Social Media Data: A Meta-Analysis. Information, 11(4), 187.
. Pradhan, V. M., Vala, J., & Balani, P. (2016). A survey on sentiment analysis algorithms for opinion mining. International Journal of Computer Applications, 133(9), 7-11.
. Sun, S., Luo, C., & Chen, J. (2017). A review of natural language processing techniques for opinion mining systems. Information fusion, 36, 10-25.
. Alharbi, N. H., & Alkhateeb, J. H. (2021, July). Sentiment Analysis of Arabic Tweets Related to COVID-19 Using Deep Neural Network. In 2021 International Congress of Advanced Technology and Engineering (ICOTEN) (pp. 1-11). IEEE.
. Wagh, R., & Punde, P. (2018, March). Survey on sentiment analysis using twitter dataset. In 2018 Second International Conference on Electronics, Communication and Aerospace Technology (ICECA) (pp. 208-211). IEEE.
. Hemmatian, F., & Sohrabi, M. K. (2019). A survey on classification techniques for opinion mining and sentiment analysis. Artificial Intelligence Review, 52(3), 1495-1545.
. Nassif, A. B., Elnagar, A., Shahin, I., & Henno, S. (2021). Deep learning for Arabic subjective sentiment analysis: Challenges and research opportunities. Applied Soft Computing, 98, 106836.
. Liu, W., Wang, Z., Liu, X., Zeng, N., Liu, Y., & Alsaadi, F. E. (2017). A survey of deep neural network architectures and their applications. Neurocomputing, 234, 11-26.
. Kolchyna, O., Souza, T. T., Treleaven, P., & Aste, T. (2015). Twitter sentiment analysis: Lexicon method, machine learning method and their combination. arXiv preprint arXiv:1507.00955.
. Yue, L., Chen, W., Li, X., Zuo, W., & Yin, M. (2019). A survey of sentiment analysis in social media. Knowledge and Information Systems, 60(2), 617-663.
. Badaro, G., Baly, R., Hajj, H., El-Hajj, W., Shaban, K. B., Habash, N., ... & Hamdi, A. (2019). A survey of opinion mining in Arabic: A comprehensive system perspective covering challenges and advances in tools, resources, models, applications, and visualizations. ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP), 18(3), 1-52.
. Bashar, A. (2019). Survey on evolving deep learning neural network architectures. Journal of Artificial Intelligence, 1(02), 73-82.
. Alom, M. Z., Taha, T. M., Yakopcic, C., Westberg, S., Sidike, P., Nasrin, M. S., ... & Asari, V. K. (2019). A state-of-the-art survey on deep learning theory and architectures. Electronics, 8(3), 292.
. Siyam, N., Alqaryouti, O., & Abdallah, S. (2020). Mining government tweets to identify and predict citizens engagement. Technology in Society, 60, 101211.
. Desai, M., & Mehta, M. A. (2016, April). Techniques for sentiment analysis of Twitter data: A comprehensive survey. In 2016 International Conference on Computing, Communication and Automation (ICCCA) (pp. 149-154). IEEE.
. Bing, L. (2012). Sentiment Analysis and Opinion Mining (Synthesis Lectures on Human Language Technologies). University of Illinois: Chicago, IL, USA.
. Alhumoud, S. (2020). Arabic Sentiment Analysis using Deep Learning for COVID-19 Twitter Data. International Journal of Computer Science and Network Security, 132-138.
. Al-Twairesh, N., Al-Khalifa, H., Al-Salman, A., & Al-Ohali, Y. (2017). Arasenti-tweet: A corpus for arabic sentiment analysis of saudi tweets. Procedia Computer Science, 117, 63-72.
. Alhumoud, S. O., Altuwaijri, M. I., Albuhairi, T. M., & Alohaideb, W. M. (2015). Survey on arabic sentiment analysis in twitter. International Science Index, 9(1), 364-368.
. Mikolov, T., Chen, K., Corrado, G., & Dean, J. (2013). Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781.
. Chakrabarti, S., Roy, S., & Soundalgekar, M. V. (2003). Fast and accurate text classification via multiple linear discriminant projections. The VLDB journal, 12(2), 170-185.
. Alhajji, M., Al Khalifah, A., Aljubran, M., & Alkhalifah, M. (2020). Sentiment analysis of tweets in Saudi Arabia regarding governmental preventive measures to contain COVID-19.
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