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Sentiment Analysis of COVID-19 Nationwide Lockdown effect in India

机译：Covid-19全国锁定效应的情感分析

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Nowadays, Sentiment Analysis has become an active research area due to the availability of many opinionated data through increased activity in Blogging, Tagging, Podcasting, social networking sites, RSS feeds, and Social Bookmarking. In the present situation, the whole world is facing the crisis of the COVID-19 pandemic. Particularly, let’s talk about nationwide lockdown in India to control the spread of COVID-19. The government relies on social media to observe people’s aviews on their policies during the lockdown. In this paper, Twitter data has been used for Sentiment Analysis, which focus on people opinion during the COVID-19 nationwide Lockdown effect in India. Different keywords data was collected on various dates between March 25, 2020, to June 09, 2020. This research work is an application of the real-time TextBlob sentiment analyzer tool built based on the Natural Language Toolkit (NLTK). Relevant keyword tweets were extracted by tweeter API. Then a model was trained to classify the result on a specific opinion. This NLPbased sentiment analysis model is ideal for analyzing the emotions while tested with seven primary keywords: Lockdown1.0, Migrant Workers, Indian Economic, ICMR, Lockdown5.0, Medical Facilities, and Police. The result shows that Lockdown 1.0 got the most positive sentiments, followed by ICMR and Medical Facility.
机译：如今，由于通过增加博客，标记，播客，社交网站，RSS源和社会书签的活动，通过增加了许多自由数据的可用数据，情感分析已成为一个活跃的研究领域。在目前的情况下，整个世界都面临着Covid-19流行病的危机。特别是，让我们谈谈印度的全国锁定，以控制Covid-19的传播。政府依赖社交媒体在锁定期间观察人们的策略。在本文中，Twitter数据已被用于情感分析，其关注在印度Covid-19的Covid-19期间的观点。在2020年3月25日至6月25日至6月25日至6月25日之间收集了不同的关键字数据。该研究工作是基于自然语言工具包（NLTK）构建的实时文本情绪分析仪工具的应用。相关的关键字推文是由Tefeeter API提取的。然后培训模型，以对特定意见进行分类结果。该NLPASED的情绪分析模型非常适合分析情绪，同时用七个主要关键词进行测试：Lockdown1.0，移民工人，印度经济，ICMR，Lockdown5.0，医疗设施和警察。结果表明，锁定1.0得到了最积极的情绪，其次是ICMR和医疗设施。

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