Title: Feature selection based on genetic algorithm and hybrid model for sentiment polarity classification
Authors: P. Kalaivani; K.L. Shunmuganathan
Addresses: Department of Computer Science and Engineering, St. Joseph's College of Engineering, Sathyabama University, Chennai, India ' Department of Computer Science and Engineering, RMK Engineering College, Chennai, India
Abstract: Sentiment classification is to find the polarity of product or user reviews. Supervised machine learning algorithms is used for opinion mining such as naive Bayes, K-nearest neighbour, decision trees, maximum entropy and hidden Markov model and support vector machine. KNN is a simple algorithm, but a less efficient classification algorithm. In this paper, we propose an improved KNN algorithm. An optimised feature selection, genetic algorithm that incorporates the information gain for feature selection and combined with bagging technique and KNN for improving the accuracy of sentiment classification. Specifically, we compared two approaches and traditional KNN for sentiment classification of movie reviews and product reviews. The same approach has been applied to other machine learning algorithms such as support vector machine and naive Bayes and the result is compared with POS-based feature set method. The proposed method is evaluated and experimental results using information gain, genetic algorithm with bagging technique indicate higher performance result with accuracy of 87.50% of the movie reviews and exhibits better performance in terms of accuracy, precision and recall for movie, DVD, electronics and kitchen reviews.
Keywords: sentiment classification; supervised machine learning; feature selection; genetic algorithms; product reviews; user reviews; movie reviews; film reviews; information gain; bagging; opinion mining; K-nearest neighbour; kNN; support vector machines; SVM; naive Bayes; electronics reviews; DVD reviews; kitchen reviews.
International Journal of Data Mining, Modelling and Management, 2016 Vol.8 No.4, pp.315 - 329
Received: 07 Mar 2014
Accepted: 05 Apr 2015
Published online: 29 Dec 2016 *