Sentiment Analysis of Restaurant Reviews Using Bagged Ensemble Classifiers

International Journal of Artificial Intelligence and Knowledge Discovery

Govindarajan Muthukumarasamy¹ 

关键词: Accuracy;    Bagging;    Genetic Algorithm (GA);    Naive Bayes (NB);    Sentiment Mining;    Support Vector Machine;   

DOI  :  

学科分类：建筑学

来源: RG Education Society

【 摘 要 】

The area of sentiment mining (also called sentiment extraction, opinion mining, opinion extraction, sentiment analysis, etc.) has seen a large increase in academic interest in the last few years. Researchers in the areas of natural language processing, data mining, machine learning, and others have tested a variety of methods of automating the sentiment analysis process. The feasibility and the benefits of the proposed approaches are demonstrated by means of restaurant review that is widely used in the field of sentiment classification. In the proposed work, a comparative study of the effectiveness of ensemble technique is made for sentiment classification. Bagging and boosting are two relatively new but popular methods for producing ensembles. In this work, bagging is evaluated on restaurant review data set in conjunction with Naive Bayes (NB), Support Vector Machine (SVM), Genetic Algorithm (GA) as the base learners. The proposed bagged NB,SVM,GAis superior to individual approaches for restaurant review in terms of classification accuracy.

【 授权许可】

Unknown