Abstract
The fast development of web sites and the number of product on these websites are available. The purpose of classification of sentiment is to efficiently identify opinion expressed in text. This paper compares three different optimized models including genetic optimized feature selection method, Genetic Algorithm (GA), ensemble approach that uses information gain and genetic algorithm as feature selection methods incorporated SVM model, Genetic Bagging (GB) and the next method uses optimized feature selection as feature selection technique incorporated back propagation model, Genetic Neural Network (GNN) models are compared. We are tested in sentiment analysis using sample multi-domain review datasets and movie review dataset.. These approaches are tested using various quality metrics and the results show that the Genetic Bagging (GB) technique outperforms in classifying the sentiment of the multi domain reviews and movie reviews. An empirical analysis is performed to compare the level of importance of the classifiers GB, GNN methods with McNemar’s statistical method.
Highlights
Sentiment analysis analyzes a person’s emotions, feelings, and behaviors from an enormous amount of subjective data in digital format
Experiments carried for multi-domain reviews and movie reviews for positive and negative reviews with different attribute weight relation using genetic algorithm, hybrid genetic NN and hybrid genetic bagging algorithm resulted in certain significant results
The NN approach of sentiment classification incorporated with a genetic algorithm improved the average accuracy of nearly 90.25%
Summary
Sentiment analysis analyzes a person’s emotions, feelings, and behaviors from an enormous amount of subjective data in digital format This information is used for the identification and classification of sources. A fundamental task in the classification of opinions is to determine the polarity of the information contained in the text file, sentence or function level for the product or service analysis. As opposed to the user-generated content, multi-domain reviews provide a more structured and less emotional text in terms of style, yet more subtle in opinion expression, rendering difficulty for analysis. The sampling technique like stratified sampling is used with the bagging, method in which comparison is done to find the one which results better To apply this approach to multi domain reviews and movie reviews. To evaluate the efficiency of these new approaches in the multi domain reviews and movie reviews
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