Novel Opinion mining System for Movie Reviews
AbstractAbstract: In this paper, an efficient opinion mining system has been presented. Opinion Mining (OM) works on transferring the online available opinions into useful knowledge. The proposed system utilizes Word2Vec, which is one of the states of the art text feature extraction method, along with ensemble learning algorithm for classification. The challenging and benchmark “IMDB Movies Reviews” dataset have been used for conducting the experimental comparison and verification. In addition, the performance of the proposed method is compared to some of the well-known machine learning algorithms like Support Vector Machine (SVM), K-Nearest Neighbor (KNN), and Naive Bayes (NB). The tested ensemble methods are the Random Forest (RF), AdaBoost Classifier, and Gradient-Boosting Classifier (GBC). The results of the conducted experiments using the challenging and benchmark “IMDB Movies Reviews” dataset have shown that the performance of SVM, KNN, and NB are comparable. However, the performance, robustness and stability of the system has been significantly improved by adapting the ensemble learning along with the Word2Vec, and an efficient preprocessing the data.
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