Abstract
At the end of 2019, the world has known the covid-19 crisis that negatively affected the health, economic, social, and psychological status of people. Since the beginning of this crisis, users express their ideas, opinions, and sentiments about the coronavirus on all social networks such as Facebook, Twitter, Instagram, etc. For example, until May 8th, 2020, the number of tweets published on Twitter is equal to 628,809,016. In this paper, our proposed method analyzes and classifies covid-19 tweets published in morocco for extracting sentiments. Our approach uses the advantages of new proposed tweets features using a dictionary-based approach and a Python library for developing a new recommendation approach. As Experiments, Our proposed approach outperforms the well-known machine learning classifiers. We find also that based on the epidemiological situation in morocco, the sentiments of Moroccan users changed.
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References
Jelodar, H., Wang, Y., Orji, R., Huang, S.: Deep sentiment classification and topic discovery on novel coronavirus or COVID-19 online discussions: NLP using LSTM recurrent neural network approach. IEEE J. Biomed. Health Inform. 24(10), 2733–2742 (2020). https://doi.org/10.1109/JBHI.2020.3001216
Aslam, F., Awan, T.M., Syed, J.H., et al.: Sentiments and emotions evoked by news headlines of coronavirus disease (COVID-19) outbreak. Humanit. Soc. Sci. Commun. 7, 23 (2020). https://doi.org/10.1057/s41599-020-0523-3
Samuel, J., Ali, G.G., Rahman, M., Esawi, E., Samuel, Y.: Covid-19 public sentiment insights and machine learning for tweets classification. Information 11(6), 314 (2020)
Pokharel, B.P.: Twitter Sentiment Analysis During Covid-19 Outbreak in Nepal, 11 June 2020) Available at SSRN https://doi.org/10.2139/ssrn.3624719, https://ssrn.com/abstract=3624719
Hung, M., et al.: Social network analysis of COVID-19 sentiments: application of artificial intelligence. J. Med. Internet Res. 22(8), e22590 (2020)
Muthusami, R., Bharathi, A., Saritha, K.: Covid-19 outbreak: tweet based analysis and visualization towards the influence of coronavirus in the world. Gedrag en Organisatie 33(2) (2020). https://doi.org/10.37896/GOR33.02/062
Manguri, K.H., Ramadhan, R.N., Mohammed Amin, P.R.: Twitter sentiment analysis on worldwide COVID-19 outbreaks. Kurdistan J. Appl. Res. 5(3), 54–65 (2020)
Chakraborty, K., Bhatia, S., Bhattacharyya, S., Platos, J., Bag, R., Hassanien, A.E.: Sentiment analysis of COVID-19 tweets by deep learning classifiers-a study to show how popularity is affecting accuracy in social media. Appl. Soft Comput. 97, 106754 (2020). https://doi.org/10.1016/j.asoc.2020.106754
Lyu, X., Chen, Z., Wu, D., Wang, W.: Sentiment analysis on Chinese Weibo regarding COVID-19. In: Zhu, X., Zhang, M., Hong, Yu., He, R. (eds.) NLPCC 2020. LNCS (LNAI), vol. 12430, pp. 710–721. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-60450-9_56
Mostafa, L.: Egyptian student sentiment analysis using Word2vec during the coronavirus (Covid-19) pandemic. In: Hassanien, A.E., Slowik, A., Snášel, V., El-Deeb, H., Tolba, F.M. (eds.) AISI 2020. AISC, vol. 1261, pp. 195–203. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-58669-0_18
Madani, Y., Erritali, M., Bengourram, J., et al.: A multilingual fuzzy approach for classifying Twitter data using fuzzy logic and semantic similarity. Neural Comput. Appl. 32, 8655–8673 (2020). https://doi.org/10.1007/s00521-019-04357-9
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Madani, Y., Erritali, M., Bouikhalene, B. (2021). Analyzing Moroccan Tweets to Extract Sentiments Related to the Coronavirus Pandemic: A New Classification Approach. In: Fakir, M., Baslam, M., El Ayachi, R. (eds) Business Intelligence. CBI 2021. Lecture Notes in Business Information Processing, vol 416. Springer, Cham. https://doi.org/10.1007/978-3-030-76508-8_3
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DOI: https://doi.org/10.1007/978-3-030-76508-8_3
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