Application of bidirectional LSTM deep learning technique for sentiment analysis of COVID-19 tweets: post-COVID vaccination era

Abstract

Abstract Background Social media platforms, especially Twitter, have turned out to be a major source of data repositories. They have become a platform that citizens can use to voice their concerns about issues that affect them. Most importantly, during the COVID-19 era, the platform was greatly used by governments and health organizations to sensitize people about the safety guidelines that they must adhere to so as to remain safe during the pandemic. As expected, people also used Twitter and other social media platforms to voice their opinions about how governments are handling the COVID-19 pandemic outbreak. Governments and organizations could, therefore, use these social media as a feedback mechanism that can help them know the view of the citizens about their policies. This could help them in making informed decisions about their policies. Aim The aim of this paper is to explore the use of BiLSTM deep learning technique for sentiment analysis of COVID-19 tweets. Methodology The study retrieved 197,327 tweets from the Nigeria Twitter domain using #COVID or #COVID-19 hashtags as keywords. The dataset was retrieved within the 1st month of COVID-19 vaccination in Nigeria, i.e., March 15–June 15, 2021. BiLSTM deep learning technique was trained using 789,306 sentiment annotated tweets obtained from Kaggle Sentiment140 tweet datasets. The preprocessed case study tweets were then used to evaluate the proposed model. Also, a precision of 78.26% and a recall value of 78.27% were also obtained. Results With an accuracy of 78.29%, 98,545 (49.93%) positive sentiments and 98,782 negative sentiments (50.06%) were recorded. Also, a precision of 78.26% and a recall value of 78.27% were also obtained. However, the presence of outliers which are tweets not related to COVID but which used the hashtag was observed. Conclusion This study has revealed the strength of BiLSTM deep learning technique for sentiment analysis. The results obtained revealed an almost balanced sentiments toward the pandemic with 49.93% positive disposition to the pandemic as compared to 50.06% negative disposition. This showed affirmed the impact of COVID vaccine in dousing citizen’s tension when it was made available for public use. However, the presence of outliers in the classified tweets could be a pointer to the reason why aspect-based sentiment analysis could be preferred to sentence-based sentiment analysis.