Article: Analysing user sentiment of Indian movie reviews: A probabilistic committee selection model.
Journal: The Electronic Library. [B class as per ABDC list, Scopus Indexed, Impact factor - 0.484]
Purpose: To be sustainable and competitive in the current business environment, it is useful to understand users’sentiment towardsproductsand services. This criticaltaskcan be achieved vianaturallanguage processingandmachinelearning classifiers. This research proposes a novel probabilistic committee selection classifier (PCC) to analyze and classify the sentiment polarities of movie reviews. Design/methodology/approach: An Indian moviereviewcorpusisconstructedforuse in this research. Another publicly available movie review polarity corpus is also involved with regard tovalidatingthe results. The greedystepwisesearchmethodisusedtoextract the features/words of the reviews. The performance of the proposed classifier is measured using different metrics, such as F-measure, false positive rate, receiver operating characteristic (ROC) curve, and training time. Further, the proposedclassifieriscomparedwith otherpopular machine-learning classifiers, such as Bayesian, Naïve Bayes, Decision Tree (J48), Support Vector Machine, and Random Forest.. Findings: The results of this study show that the proposed classifier is good at predicting the positive or negative polarity of the movie reviews. Performance accuracy and the value of the ROC curve of the PCC is found to be most suitable of all other classifiers tested in this study. This classifierisalsofoundtobeefficientatidentifyingpositivesentimentsof reviews, where it gives low false positive rates for both Indian Movie Review and Review Polarity corpora used in this study. The training time of the proposed classifier is found to be slightly high from Bayesian and Naïve Bayes and J48.
Keywords:Probabilistic committee selection, Sentiment analysis, Indian movie review, Machine learning classifiers, Greedy stepwise search method.