论文标题

Covid-19问题中文本的意见挖掘以及ML,Bert&RNN的比较研究

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

论文作者

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

论文摘要

全球世界正在跨越大流行状况,这是呼吸道综合症的灾难性爆发,被公认为Covid-19。这是整个212个国家的全球威胁,人们每天都会与强大的情况相遇。相反,成千上万的感染者生活在山上。心理健康也受到这种冠状病毒状况的影响。由于这种情况,在线资源成为了一个沟通的地方,普通人在任何议程中都有意见。例如与新闻有关的积极和负面,金融问题,国家和家庭危机,缺乏进出口的收入系统等。各种情况是最近的时尚新闻。因此,在瞬间产生了大量文本,因此,在次大陆地区,与其他国家的情况相同,人们对文本和情况的看法也一样,但语言不同。本文提出了一些特定的输入以及来自各个来源的孟加拉文本评论,这些评论可以确保一个插图的目标,即机器学习结果能够构建辅助系统。意见挖掘辅助系统在所有语言偏好中都可能影响。据我们所知,本文预测了有关COVID-19问题的Bangla输入文本提出的ML算法和深度学习模型分析也通过比较分析来检查未来的可达到性。比较分析指出,有关文本预测准确性的报告为91%,ML算法和79%以及深度学习模型。

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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