A Voting classification approach for Sentiment Extraction from Bengali text

Abstract

Sentiment extraction is one of the most challenging tasks in Natural Language Processing (NLP). It is essential for analysing consumer and user feedback on social media sites and in the commercial world. Finding sentiments or emotions in raw text data and identifying their polarity, or whether they are positive or negative, is the main objective of sentiment extraction. This area has been the focus of various research projects for English and other significant natural languages. In this article, we offer a voting classification method that uses a variety of machine learning classifiers to extract sentiment from Bengali language text. We explored Logistic Regression, Decision Tree Classifier, Random Forest Classifier, Support Vector Classifier, Multinomial Nave Base and Ridge Classifier, and lastly, we used a voting classification strategy to extract sentiments from social media comments.