With the rapid growth of websites and web form the number of product reviews is available on the sites. An opinion mining system is needed to help the people to evaluate emotions, opinions, attitude, and behavior of others, which is used to make decisions based on the user preference. In this paper, we proposed an optimized feature reduction that incorporates an ensemble method of machine learning approaches that uses information gain and genetic algorithm as feature reduction techniques. We conducted comparative study experiments on multidomain review dataset and movie review dataset in opinion mining. The effectiveness of single classifiers Naïve Bayes, logistic regression, support vector machine, and ensemble technique for opinion mining are compared on five datasets. The proposed hybrid method is evaluated and experimental results using information gain and genetic algorithm with ensemble technique perform better in terms of various measures for multidomain review and movie reviews. Classification algorithms are evaluated using McNemar’s test to compare the level of significance of the classifiers.
Sentiment classification is to find the polarity of product or user reviews. Supervised machine learning algorithms are used for opinion mining such as Navie Bayes, K-nearest neighbor and Support vector machine. KNN is simple algorithm but less efficient classification algorithm. In this paper we propose an improved KNN algorithm, genetic algorithm is developed which is a hybrid genetic algorithm that incorporates the information gain for feature selection and combined with KNN to improve its classification performance. Specifically, we compared other supervised machine learning approaches such as Navie Bayes and traditional kNN for Sentiment Classification of movie reviews and book reviews. The experimental results using genetic algorithm with improved indicate high performance levels with Fmeasure of over 87% on the movie reviews.
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