Optimization of Sentiment Analysis Methods via Machine Learning Algorithms

Abstract

This research work focus on the latest studies that have used Machine learning to find a solution of sentiment analysis problems related to sentiment polarization. In preprocessing steps, the Models applied stop words and Bag of Words to a collection of datasets. Even with the widespread usage and acceptance of some approaches, a superior technique for categorizing the polarization of a text documents is tough to make out. Machine learning has lately evoked the attention as a method for sentiment investigation. The present work proposes a machine learning based hybrid algorithm that incorporate N-gram technique as a feature extraction and combines Decision tree classifier and Random forest Classifier techniques as a classification for sentiment analysis. Naïve bayse, linear classifier and support vector machine approaches are perform in the perspective of sentiment classification. Finally, a comparative study with the different supervised algorithm is implemented on product reviews dataset. The performance of models are evaluated on confusion matrix. In the comparative analysis of classification techniques, the combined technique has shown better results than previously used supervised techniques of naïve bayse ,linear classifier and support vector machine. &nbsp