Machine Learning Based Anxiety Prediction of General Public from Tweets During COVID-19

Tribedi, Shubham; Biswas, Anuraag; Ghosh, Swarup Kr; Ghosh, Anupam

doi:10.1007/978-3-030-74761-9_13

Cited by 3 publications

(1 citation statement)

References 22 publications

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“…Nair et al 2021) for mining public opinion from COVID-19 tweets to help out public health organizations, as well as government officials [12]. In [4], [13]- [17], the authors had used Multinomial NB, Decision Tree (DT), Logistic Regression, Support Vector Machine (SVM), Random Forest Classifier with different features like weighted TF-IDF and n-gram to investigate sentiment analysis from the tweets related to COVID-19 pandemic to mine public anxiety.…”

Section: Related Workmentioning

confidence: 99%

A Deep Learning Approach for Public Sentiment Analysis in COVID-19 Pandemic

HOSSAIN¹,

Assaduzzaman²,

Mynoddin³

et al. 2022

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Sentiment analysis is a process of extracting opinions into the positive, negative, or neutral categories from a pool of text using Natural Language Processing (NLP). In the recent era, our society is swiftly moving towards virtual platforms by joining virtual communities. Social media such as Facebook, Twitter, WhatsApp, etc are playing a very vital role in developing virtual communities. A pandemic situation like COVID-19 accelerated people's involvement in social sites to express their concerns or views regarding crucial issues. Mining public sentiment from these social sites especially from Twitter will help various organizations to understand the people's thoughts about the COVID-19 pandemic and to take necessary steps as well. To analyze the public sentiment from COVID-19 tweets is the main objective of our study. We proposed a deep learning architecture based on Bidirectional Gated Recurrent Unit (BiGRU) to accomplish our objective. We developed two different corpora from unlabelled and labeled COVID-19 tweets and use the unlabelled corpus to build an improved labeled corpus. Our proposed architecture draws a better accuracy of 87% on the improved labeled corpus for mining public sentiment from COVID-19 tweets.

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