Sentiment analysis on Hindi tweets during COVID-19 pandemic

Abstract

A gap among the people has been created due to a lack of social interactions. The physical void has led to an increase in online interaction among users on social media platforms. Sentiment analysis of such interactions can help us analyze the general public psychology during the pandemic. However, the lack of data in non-English and low-resource languages like ‘Hindi’ makes it difficult to study it among native and non-English speaking masses. Here, we create a small collection of ‘Hindi’ tweets on COVID-19 during the pandemic containing 10,011 tweets for sentiment analysis, which is named as sentiment analysis for Hindi (SAFH). In this article, we describe the process of collecting, creating, annotating the corpus, and sentiment classification. The claims have been verified using different word embedding with a deep learning classifier through the proposed model. The achieved accuracy of the proposed model yields up to a permissible rate of 90.9%.