In this paper, the sentiment classification approaches are introduced in Indian banking,
governmental and global news. The study assesses state-of-art deep contextual language
representation, DistilBERT, and traditional context-independent system, TF-IDF, on
multiclass (positive, negative, and neutral) sentiment classification news-events. The
DistilBERT model is fine-tuned and fed into four supervised machine learning classifiers
Random Forest, Decision Tree, Logistic Regression, and Linear SVC, and similarly with …

In this paper, the sentiment classification approaches are introduced in Indian banking,
governmental and global news. The study assesses state-of-art deep contextual language
representation, DistilBERT, and traditional contextindependent system, TF-IDF, on multiclass
(positive, negative, and neutral) sentiment classification news-events. The DistilBERT model
is fine-tuned and fed into four supervised machine learning classifiers Random Forest,
Decision Tree, Logistic Regression, and Linear SVC, and similarly with baseline TF-IDF. The …