{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,18]],"date-time":"2025-12-18T19:57:25Z","timestamp":1766087845067,"version":"3.41.2"},"reference-count":0,"publisher":"World Scientific Pub Co Pte Ltd","issue":"01","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Int. J. Artif. Intell. Tools"],"published-print":{"date-parts":[[2022,2]]},"abstract":"<jats:p> The novel Coronavirus (COVID-19) has affected the normal life of people around the world and has forced them to maintain social distance, self-quarantine and closing of many businesses. So, the people wish to share the information regarding the pandemic through social media e.g. Twitter. The key objective of this paper is to identify the sentiment associated with the pandemic by utilizing the tweets regarding the Coronavirus (COVID-19) and by using the python libraries to perform the task. We analyze the sentiment of people when Coronavirus has attained a peak level using the machine, deep learning techniques and TextBlob. We have classified the sentiments into positive and negative classes using the Machine Learning (NB, SVM, Logistic regression) approaches and deep learning-based Bi-LSTM model. The Bi-LSTM approach has achieved better accuracy (0.87) compared to the traditional machine learning models for Twitter sentiment classification. From the overall analysis, we could conclude that people are confident and they express more positivity towards the recovery from the COVID-19 pandemic. Such an analysis will help the policy and decision-makers to address the needs of the public appropriately. <\/jats:p>","DOI":"10.1142\/s0218213022500117","type":"journal-article","created":{"date-parts":[[2022,2,24]],"date-time":"2022-02-24T08:07:21Z","timestamp":1645690041000},"source":"Crossref","is-referenced-by-count":30,"title":["Deep Learning and TextBlob Based Sentiment Analysis for Coronavirus (COVID-19) Using Twitter Data"],"prefix":"10.1142","volume":"31","author":[{"given":"Ganesh","family":"Chandrasekaran","sequence":"first","affiliation":[{"name":"Department of ECE, Karunya Institute of Technology and Sciences, Coimbatore, Tamilnadu, India"}]},{"given":"Jude","family":"Hemanth","sequence":"additional","affiliation":[{"name":"Department of ECE, Karunya Institute of Technology and Sciences, Coimbatore, Tamilnadu, India"}]}],"member":"219","published-online":{"date-parts":[[2022,2,28]]},"container-title":["International Journal on Artificial Intelligence Tools"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.worldscientific.com\/doi\/pdf\/10.1142\/S0218213022500117","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,2,28]],"date-time":"2022-02-28T01:07:00Z","timestamp":1646010420000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.worldscientific.com\/doi\/abs\/10.1142\/S0218213022500117"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,2]]},"references-count":0,"journal-issue":{"issue":"01","published-print":{"date-parts":[[2022,2]]}},"alternative-id":["10.1142\/S0218213022500117"],"URL":"https:\/\/doi.org\/10.1142\/s0218213022500117","relation":{},"ISSN":["0218-2130","1793-6349"],"issn-type":[{"type":"print","value":"0218-2130"},{"type":"electronic","value":"1793-6349"}],"subject":[],"published":{"date-parts":[[2022,2]]},"article-number":"2250011"}}