Evaluation of Different Machine Learning Methods for Sentiment Analysis of Indian Languages

The volume of information accessible via websites is consistently and substantially increasing on a global scale. The primary issue is not the sharing of data but rather the continuous demand for increased knowledge among individuals. Research and development in sentiment analysis is a top priority within the domain of data mining. As a direct consequence of the exponential growth of social media, individuals' capacity to express their emotions and thoughts online has grown substantially. Indian languages are frequently employed in interpersonal communication. In recent decades, sentiment analysis (SA) has garnered considerable attention as a result of its practicality in assessing public sentiment and perception on a variety of subjects. However, sentiment analysis remains deficient in the Indian languages of India. The objective of this study is to conduct an assessment of machine learning (ML) methodologies in the context of sentiment analysis. We present a comparison of existing techniques in terms of accuracy, and we also discuss future research directions in the field of sentiment analysis.