An Ensemble Based Approach for Sentiment Classification in Asian Regional Language.

In today's digital world, millions of individuals are linked to one another via the Internet and social media. This opens up new avenues for information exchange with others. Sentiment analysis (SA) has gotten a lot of attention during the last decade. We analyse the challenges of Sentiment Analysis (SA) in one of the Asian regional languages known as Marathi in this study by providing a benchmark setup in which we first produced an annotated dataset composed of Marathi text acquired from microblogging websites such as Twitter. We also choose domain experts to manually annotate Marathi microblogging posts with positive, negative, and neutral polarity. In addition, to show the efficient use of the annotated dataset, an ensemble-based model for sentiment analysis was created. In contrast to others machine learning classifier, we achieved better performance in terms of accuracy for ensemble classifier with 10-fold cross-validation (cv), outcomes as 97.77%, f-score is 97.89%. [ABSTRACT FROM AUTHOR]