Sentiment Analysis on COVID-19 News Videos Using Machine Learning Techniques

ABSTRACT

Coronavirus disease (COVID-19) has affected all walks of human life most adversely, from entertainment to education. The whole world is confronting this deadly virus, and no country in this world remains untouched during this pandemic. From the early days of reporting this virus from many parts of the world, many news videos on the same got uploaded in various online platforms such as YouTube, Dailymotion, and Vimeo. Even though the content of many of those videos was unauthentic, people watched them and expressed their views and opinions as comments. Analysing these comments can unearth the patterns hidden in them to study people’s responses to videos on COVID-19. This paper proposes a sentiment analysis approach on people’s response towards such videos, using text mining and machine learning. This work implements different machine learning algorithms to classify people’s sentiments and also uses text mining principles for finding out several latent themes, from the comments collected from YouTube. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.