Deep Word Embedding and Language Model based Sentimental Analysis of COVID-19 Tweets

The great Covid-19 pandemic affected billions of people's lives personally and socially. The research involves the analysis of public's views and opinions shared on Twitter social media platform related to Covid-19 pandemic and its detrimental or non-detrimental effects on public's mental health by using machine learning algorithm. The main purpose of this research includes analyzing public views related to Covid-19 pandemic by classifying the Tweets collected from the Twitter social platform. The proposed approach combines deep word embedding with MiniLM as an encoder to produce word vectors of high-dimensionality to preserve the words' semantic information. The resultant word vectors were used to train the model for the classification of the tweet in five sentiments i.e. Positive, Extremely Positive, Negative, Extremely Negative, and Neutral. The methodology is tested using publicly available Kaggle dataset as well as privately collected tweets. The comparative evaluation of the models revealed that MiniLM outperformed existing BERT based counterparts and attained highest accuracy of 93% with the Kaggle dataset. This analysis can assist the medical health authorities to monitor health information, conduct, and plan interventions to lower the pandemic effect and can help government to take precautionary measures.