Sentiment Analysis Using CatBoost Algorithm on COVID-19 Tweets

ABSTRACT

Sentimental analysis is a study of emotions or analysis of text as an approach to machine learning. It is the most well-known message characterization device that investigates an approaching message and tells whether the fundamental feeling is positive or negative. Sentimental analysis is best when utilized as an instrument to resolve the predominant difficulties while solving a problem. Our main objective is to identify the emotional tone and classify the tweets on COVID-19 data. This paper represents an approach that is evaluated using an algorithm namely—CatBoost and measures the effectiveness of the model. We have performed a comparative study on various machine learning algorithms and illustrated the performance metrics using a Bar-graph. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.