Sentiment Analysis of Nepali COVID-19 Tweets using BERT-LSTM

Abstract

The global impact of COVID-19 has significantly reshaped the day-by-day lives of individuals worldwide. COVID-19 is one of the top deadly diseases and has tragically claimed the lives of millions across the globe. The people are affected not only by the physical infection but also mentally. Among the various social media platforms, Twitter is a widely utilized medium, reflecting a substantial surge in discussions about the coronavirus. These discussions encompass a spectrum of positive, negative, and neutral sentiments. The sentiments acknowledged by individuals, encapsulated in their posts and tweets across this platform, offer valuable insights into their emotional states and perspectives. In this investigation, the people's sentiments using Nepali COVID-19-related Twitter datasets are inspected. For this, the approach involves a two-step process. Initially, the multilingual BERT(m-bert) model will utilize whose output is used for subsequent downstream tasks. Secondly, m-BERT's output is connected to the LSTM layer to categorize the people's sentiments. The model was trained and tested using publically available NepCov19Tweets datasets. These tweets were split into three groups (positive, negative, and neutral). The appraisal outcomes for NepCOV19Tweets demonstrate that the proposed model comes up with outstanding performance when compared to the existing model, achieving an average accuracy of 76.04%, 80.03% recall, a precision of 77.12%, and an F1-score of 76%.