Analysis of sentiments on the onset of Covid-19 using Machine Learning Techniques
Abstract The novel coronavirus (Covid-19) pandemic has struck the whole world and is one of the most striking topics on social media platforms. Sentiment outbreak on social media enduring various thoughts, opinions, and emotions about the Covid-19 disease, expressing views they are feeling presently. Analyzing sentiments helps to yield better results. Gathering data from different blogging sites like Facebook, Twitter, Weibo, YouTube, Instagram, etc., and Twitter is the largest repository. Videos, text, and audio were also collected from repositories. Sentiment analysis uses opinion mining to acquire the sentiments of its users and categorizes them accordingly as positive, negative, and neutral. Analytical and machine learning classification is implemented to 3586 tweets collected in different time frames. In this paper, sentiment analysis was performed on tweets accumulated during the Covid-19 pandemic, Coronavirus disease. Tweets are collected from the Twitter database using Hydrator a web-based application. Data-preprocessing removes all the noise, outliers from the raw data. With Natural Language Toolkit (NLTK), text classification for sentiment analysis and calculate the score subjective polarity, counts, and sentiment distribution. N-gram is used in textual mining -and Natural Language Processing for a continuous sequence of words in a text or document applying uni-gram, bi-gram, and tri-gram for statistical computation. Term frequency and Inverse document frequency (TF-IDF) is a feature extraction technique that converts textual data into numeric form. Vectorize data feed to our model to obtain insights from linguistic data. Linear SVC, MultinomialNB, GBM, and Random Forest classifier with Tfidf classification model applied to our proposed model. Linear Support Vector classification performs better than the other two classifiers. Results depict that RF performs better.
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Yazdavar, A. H., Mahdavinejad, M. S., Bajaj, G., Thirunarayan, K., Pathak, J., and Sheth, A., 2018. Mental Health Analysis Via social media Data: 459-460. IEEE International Conference on Healthcare Informatics (ICHI).
Alamoodi A. H., Zaidan B. B., Zaidan A. A., Albahri O. S., Mohammed K. I., Malik R. Q., Almahdi E. M., Chyad M. A., Tareq Z., Albahri A. S, Hameed H., and Alaa M., 2020. Sentiment analysis and its applications in fighting COVID-19 and infectious diseases: A systematic review. Expert systems with applications, 167, 114155.
Aldarwish, M. M., and Ahmad, H. F., 2017. Predicting Depression Levels Using Social Media Posts, 277-280. IEEE 13th International Symposium on Autonomous Decentralized System (ISADS).
Baheti, R. R., and Kinariwala, S., 2019. Detection and Analysis of Stress using Machine Learning Techniques. International Journal of Engineering and Advanced Technology.
Bania, R. K., 2020. COVID-19 Public Tweets Sentiment Analysis using TF-IDF and Inductive Learning Models, 19(2), 23–41. INFOCOMP Journal of Computer Science.
Calderon-Vilca, H. D., Wun-Rafael, W. I., and Miranda-Loarte, R., 2018. Simulation of suicide tendency by using machine learning: 1-6. 36th International Conference of the Chilean Computer Science Society, SCCC 2017.
Cavazos-Rehg, P. A., Krauss, M. J., Sowles, S., Connolly, S., Rosas, C., Bharadwaj, M., and Bierut, L. J., 2016. A content analysis of depression-related Tweets, 54, 351–357. Computers in human behavior.
Chancellor, S., and De Choudhury, M., 2020. Methods in predictive techniques for mental health status on social media: a critical review, 3, 43. npj Digit. Med..
Cho, G., Yim, J., Choi, Y., Ko, J., and Lee, S. H., 2019. Review of Machine Learning Algorithms for Diagnosing Mental Illness. .16(4), 262-269. Psychiatry investigation.
Das, B., and Chakraborty, S., 2018. An Improved Text Sentiment Classification Model Using TF-IDF and Next Word Negation. ArXiv.
Desai, M., and Mehta, M. A., 2016. Techniques for sentiment analysis of Twitter data: A comprehensive survey, 149-154. International Conference on Computing, Communication and Automation (ICCCA).
Deshpande, M., and Rao, V., 2017. Depression detection using emotion artificial intelligence, 858-862. International Conference on Intelligent Sustainable Systems (ICISS).
Dubey A., 2020. Twitter Sentiment Analysis during COVID-19 Outbreak. SSRN Electronic Journal.
Jawad, A., Hodhod, R. A., and Omar, Y. M., 2018. Sentiment Analysis of Social Media Networks Using Machine Learning, 174-176. International Computer Engineering Conference (ICENCO).
Ghaderi, A.. Frounchi, J., and Farnam, A., 2015. Machine learning-based signal processing using physiological signals for stress detection: 93-98. 22nd Iranian Conference on Biomedical Engineering (ICBME).
Hatton, C. M., Paton, L. W., McMillan, D., Cussens, J., Gilbody, S., and Tiffin, P. A., 2019. Predicting persistent depressive symptoms in older adults: A machine learning approach to personalised mental healthcare, 246: 857-860. Journal of affective disorders.
Hussain, J., Ali, M., Bilal, H. S. M., Afzal, M., Ahmad, H. F., Banos, O., and Lee, S., 2015. SNS Based Predictive Model for Depression, 349–354. Lecture Notes in Computer Science.
Kemp, S., 2021. Global Overview Report.
Khan, M., Rizvi, Z., Shaikh, M. Z., Kazmi, W., and Shaikh, A., 2014. Design and Implementation of Intelligent Human Stress Monitoring System, 179-190. International Journal of Innovation and Scientific Research.
Lech, M., 2018. Detection of Adolescent Depression from Speech Using Optimized Spectral Roll-Off Parameters. Biomedical Journal of Scientific & Technical Research.
Nguyen, T., Phung, D., Dao, B., Venkatesh, S., and Berk, M., 2014. Affective and Content Analysis of Online Depression Communities, (5/3), 217-226. IEEE Transactions on Affective Computing.
Pranckevicius, T., and Marcinkevicius, V., 2017. Comparison of Naive Bayes, Random Forest, Decision Tree, Support Vector Machines, and Logistic Regression Classifiers for Text Reviews Classification. Balt. J. Mod. Comput., 5.
Ramalingam, D., Sharma, V., and Zar, P., 2019. Study of Depression Analysis using Machine Learning Techniques, 8, 7C2: 2278-3075. International Journal of Innovative Technology and Exploring Engineering (IJITEE).
Rathi, M., Malik, A., Varshney, D., Sharma, R., and Mendiratta, S., 2018. Sentiment Analysis of Tweets Using Machine Learning Approach, 1-3. Eleventh International Conference on Contemporary Computing (IC3).
Raichur, N., Lonakadi, N., and Mural, P., 2017. Detection of Stress Using Image Processing and Machine Learning Techniques, 9, 1-8. International journal of engineering and technology.
Samuel, J., Ali, G.G.M.N., Rahman, M.M., Esawi, E., and Samuel, Y., 2020. COVID-19 Public Sentiment Insights and Machine Learning for Tweets Classification, 11(6):314. Information.
Shatte A., Hutchinson D.M., and Teague S.J., 2019. Machine learning in mental health: a scoping review of methods and applications, 49(9):1426-1448. Psychological medicine.
Stolar M, N., Lech, M., Stolar S.J., and Allen N.B., 2018. Detection of Adolescent Depression from Speech Using Optimised Spectral Roll-Off Parameters. 5(1). BJSTR.
Subhani, A. R., Mumtaz, W., Saad, M. N. B. M., Kamel, N., and Malik, A. S., 2017. Machine Learning Framework for the Detection of Mental Stress at Multiple Levels, 5, 13545-13556. IEEE Access.
Tate, A. E., McCabe, R. C., Larsson, H., Lundström, S., Lichtenstein, P., and Kuja-Halkola, R., 2020. Predicting mental health problems in adolescence using machine learning techniques, 15(4), e0230389.
Troussas, C., Virvou, M., Espinosa, K. J., Llaguno, K., and Caro, J., 2013. Sentiment analysis of Facebook statuses using Naive Bayes Classifier for language learning, 1-6. IISA.
Vuppalapati, C., Khan, M. S., Raghu, N., Veluru, P., and Khursheed, S., 2018. A System to Detect Mental Stress Using Machine Learning and Mobile Development: 161-166. International Conference on Machine Learning and Cybernetics (ICMLC).
World Health Organization, 2021. Depression is a mental disorder.
Worldometers, 2020 - https://www.worldometers.info/coronavirus/
Yazdavar, A. H., Mahdavinejad, M. S., Bajaj, G., Thirunarayan, K., Pathak, J., and Sheth, A., 2018. Mental Health Analysis Via social media Data: 459-460. IEEE International Conference on Healthcare Informatics (ICHI).
Arya, V., Mishra, A. K. M., & González-Briones, A. (2022). Analysis of sentiments on the onset of Covid-19 using Machine Learning Techniques. ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal, 11(1), 45–63. https://doi.org/10.14201/adcaij.27348
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