COVID-19 Tweets Analysis through Transformer Language Models

• URL: http://arxiv.org/abs/2103.00199v1
• Date: Sat, 27 Feb 2021 12:06:33 GMT
• Title: COVID-19 Tweets Analysis through Transformer Language Models
• Authors: Abdul Hameed Azeemi, Adeel Waheed
• Abstract summary: In this study, we perform an in-depth, fine-grained sentiment analysis of tweets in COVID-19. A trained transformer model is able to correctly predict, with high accuracy, the tone of a tweet. We then leverage this model for predicting tones for 200,000 tweets on COVID-19.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Understanding the public sentiment and perception in a healthcare crisis is essential for developing appropriate crisis management techniques. While some studies have used Twitter data for predictive modelling during COVID-19, fine-grained sentiment analysis of the opinion of people on social media during this pandemic has not yet been done. In this study, we perform an in-depth, fine-grained sentiment analysis of tweets in COVID-19. For this purpose, we perform supervised training of four transformer language models on the downstream task of multi-label classification of tweets into seven tone classes: [confident, anger, fear, joy, sadness, analytical, tentative]. We achieve a LRAP (Label Ranking Average Precision) score of 0.9267 through RoBERTa. This trained transformer model is able to correctly predict, with high accuracy, the tone of a tweet. We then leverage this model for predicting tones for 200,000 tweets on COVID-19. We then perform a country-wise analysis of the tone of tweets, and extract useful indicators of the psychological condition about the people in this pandemic.

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