Feature Selection based on Genetic Algorithm and Hybrid Model for Opinion Mining

[摘要] 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 neighbor, 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 optimized feature selection, genetic algorithm that incorporates the information gain for feature reduction and combined with bagging technique. The new method improves the accuracy of sentiment classification. Specifically, we compared two approaches with PCA feature reduction technique and traditional KNN for Sentiment Classification of movie reviews. The same approach has been applied to other machine learning algorithms such as Support Vector Machine and Naïve Bayes. 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, Book, DVD, Electronics and Kitchen reviews.

[学科分类] 建筑学

[关键词] Sentiment classification;supervised machine learning algorithm;feature selection;genetic algorithm;review;Information gain;bagging