Naïve Bayes Classification of Sentiments on Subset using Tweets-during Covid-19
Keywords:
Social Media Data, Twitter, Sentiment Analysis, Machine Learning Algorithms, naïve Bayes Algorithm, Text ProcessingAbstract
Now a day, Social Media create a platform for almost all people for sharing and communicating with one another. Most of the business people and the organizations avail the social media conversation for their product promotion or predicting people behavior. The popular Social Media Networks are Facebook, Twitter, LinkedIn of Social Networks, Instagram, YouTube of Media Sharing Networks, Whatsapp, Pinterest and tripAdvisor of Consumer Review Networks. A Text Mining tool, Sentiment Analysis can help us to predict and classify the susceptible text used in the social media conversation. Even though having lots of advantages, unfortunately we have many risks in the usage of the social media content. Any individual must follow the rules and regulations for accessing the content in the social media networks. The objective of this research paper is to understand the various techniques involved in Sentiment Analysis process and choose to apply naïve bayes machine learning model in the subset level using twitter data to classify the sentiments of people in a best way.
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Divya Bharathi G, Dr.Jagan A, Pradeep Kumar V, (2020). Toxic Sentiment Identification using R Programming., International Journal of Engineering Technology and Management Sciences[IJETMS].
Zafarani, R., Abbasi, M.A., Liu, H, (2014). Social Media Mining: An Introduction.Cambridge University Press. Cambridge University Press, ISBN 978-1-107-01885-3.
DoaaMohey El-Din Mohamed Hussein, (2016). A survey on sentiment analysis challenges. Journal of King Saud University – Engineering Sciences.
Arielle Hesse, Leland Glenna, Clare Hinrichs, Robert Chiles, Carolyn Sachs, (2019). Qualitative Research Ethics in the Big Data Era. American Behavioral Scientist.
Casey Fiesler, Nicholas Proferes., (2018). Participant Perceptions of Twitter Research Ethics. Social Media + Society.
Alexandra Balahur, (2013). Sentiment Analysis in Social Media Texts. Proceedings of the 4th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis, Association for Computational Linguistics, Atlanta, Georgia, pages 120–128.
Tim O’keefe, Irena Koprinska.,(2009). Feature Selection and Weighting Methods in Sentiment Analysis. Proceedings of the 14th Australasian Document Computing Symposium, Sydney, Australia.
Erik Tromp, Mykolapechenizkiy, Mohamed Medhat Gaber., (2017). Expressive modeling for trusted big data analytics: techniques and applications in sentiment analysis. Tromp et al. Big Data Analytics.
Michael Zimmer., (2018). Addressing Conceptual Gaps in Big Data Research Ethics: An Application of Contextual Integrity. Social Media + Society.
Brett Lantz, (2013). Machine Learning with R. Packt Publishing Ltd, ISBN 978-1-78216-214-8.
Widiastuti, N. I.,(2018). Deep Learning – Now and Next in Text Mining and Natural Language Processing. INCITEST, IOP Conf. Series: Materials Science and Engineering.
Sahar Sohangir, Dingding Wang, Anna Pomeranets And Taghi, M. Khoshgoftaar.,(2018). Big Data: Deep Learning for financial sentiment analysis. Sohangir et al. J Big Data.
Ha Sung Hwang., (2019). Why Social Comparison on Instagram Matters: Its impact on Depression. KSII Transactions on Internet and Information Systems.
Concetta Papapicco., (2019). WhatsApp at Work? Instant Messaging Improves Professional Well-Being. SRG International Journal of Communication and Media Science (SSRG-IJCMS) - Volume 6 Issue 1.
Chinthapanti Bharath Sai Reddy, Kowshik, S., Rakesh Kumar, M.V., O.Nikhil Kumar Reddy, Gopichand, G., (2020). Analysing and Predicting the Emotion of WhatsApp Chats Using Sentiment Analysis. ISSN: 0193-4120.
Abubakar Ahmad, Nuhu Abdulhafiz, A. And Abubakar Abdulkadir. (2021). A Comprehensive Data Analysis on FUDMA ASUU Whatsapp Group Chat. FUDMA Journal of Sciences (FJS), ISSN online: 2616-1370, ISSN print: 2645 – 2944, Vol. 5 No. 2, pp. 26 – 33.
Saikat Dutt, Subramanian Chandramouli, Amit Kumar Das, (2019). Machine Learning. Pearson India Education Services Pvt.Ltd, ISBN 978-93-530-6669-7.
ReferenceLink-https://www.kaggle.com/datasets/datatattle/covid-19-nlp-text-classification.
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