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arXiv · 2201.02119

An Opinion Mining of Text in COVID-19 Issues along with Comparative Study in ML, BERT & RNN

Md. Mahadi Hasan Sany·Mumenunnesa Keya·Sharun Akter Khushbu·Akm Shahariar Azad Rabby·Abu Kaisar Mohammad Masum
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Abstract

The global world is crossing a pandemic situation where this is a catastrophic outbreak of Respiratory Syndrome recognized as COVID-19. This is a global threat all over the 212 countries that people every day meet with mighty situations. On the contrary, thousands of infected people live rich in mountains. Mental health is also affected by this worldwide coronavirus situation. Due to this situation online sources made a communicative place that common people shares their opinion in any agenda. Such as affected news related positive and negative, financial issues, country and family crisis, lack of import and export earning system etc. different kinds of circumstances are recent trendy news in anywhere. Thus, vast amounts of text are produced within moments therefore, in subcontinent areas the same as situation in other countries and peoples opinion of text and situation also same but the language is different. This article has proposed some specific inputs along with Bangla text comments from individual sources which can assure the goal of illustration that machine learning outcome capable of building an assistive system. Opinion mining assistive system can be impactful in all language preferences possible. To the best of our knowledge, the article predicted the Bangla input text on COVID-19 issues proposed ML algorithms and deep learning models analysis also check the future reachability with a comparative analysis. Comparative analysis states a report on text prediction accuracy is 91% along with ML algorithms and 79% along with Deep Learning Models.

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Md. Mahadi Hasan Sany, Mumenunnesa Keya, Sharun Akter Khushbu, Akm Shahariar Azad Rabby, Abu Kaisar Mohammad Masum. 2022-01-06. An Opinion Mining of Text in COVID-19 Issues along with Comparative Study in ML, BERT & RNN. https://arxiv.org/abs/2201.02119

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