Version 1
: Received: 28 April 2022 / Approved: 29 April 2022 / Online: 29 April 2022 (07:42:20 CEST)
How to cite:
HOSSAIN, G.S.; Assaduzzaman, S.; Mynoddin, M.; Sarker, I.H. A Deep Learning Approach for Public Sentiment Analysis in COVID-19 Pandemic. Preprints2022, 2022040288. https://doi.org/10.20944/preprints202204.0288.v1
HOSSAIN, G.S.; Assaduzzaman, S.; Mynoddin, M.; Sarker, I.H. A Deep Learning Approach for Public Sentiment Analysis in COVID-19 Pandemic. Preprints 2022, 2022040288. https://doi.org/10.20944/preprints202204.0288.v1
HOSSAIN, G.S.; Assaduzzaman, S.; Mynoddin, M.; Sarker, I.H. A Deep Learning Approach for Public Sentiment Analysis in COVID-19 Pandemic. Preprints2022, 2022040288. https://doi.org/10.20944/preprints202204.0288.v1
APA Style
HOSSAIN, G.S., Assaduzzaman, S., Mynoddin, M., & Sarker, I.H. (2022). A Deep Learning Approach for Public Sentiment Analysis in COVID-19 Pandemic. Preprints. https://doi.org/10.20944/preprints202204.0288.v1
Chicago/Turabian Style
HOSSAIN, G.S., Md Mynoddin and Iqbal H. Sarker. 2022 "A Deep Learning Approach for Public Sentiment Analysis in COVID-19 Pandemic" Preprints. https://doi.org/10.20944/preprints202204.0288.v1
Abstract
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.
Keywords
Sentiment Analysis; Deep Learning; BiGRU; Word2Vec
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
