Comparative Analysis of Bagging and Boosting Algorithms for Sentiment Analysis.

Sentiment analysis has become a state-of-the-art to make products market adaptive and understand the market gaps to launch updates and variants of a product entity. The contributions of this paper are providing prior point of sale based key features to generate rule based algorithms to predict the attitude and opinion implied. Amazon food reviews data has been used in the paper. We have done a comparative analysis of Random Forest and Extreme Gradient Boosting model using Bag of Words(BoW) and Term Frequency-Inverse Document Frequency(TFIDF) featurizations. [ABSTRACT FROM AUTHOR]