Classifying User Reviews of Movie Applications using Improved Logistic Regression

Abstract: In recent years review classification, analysis and prediction are one of the most commonapplications of sentiment analysis. It involves detection of sentiments on the reviews made bythe users on social networking applications through opinion mining.In general,reviews canhave positive, negative or neutral polarity indicators. For classification, the polarity indicatorstake the form of certain words and emotions that readily show the user’s sentiments. Existingworks fall short of producing accurate classification results because of two-class problem thataffects the performance of evaluation parameters like precision, recall, accuracy and F-measure.Hencethereisaneedofanefficientclassificationtechniquewhichaddressestwo-classproblem. Thiswork proposes ImprovedversionofLogisticRegression[ILR]thatiscommonly used for sentiment analysis and classification. The proposed classification techniqueidentifies and replaces the misspelled words in the sentence,supportcountestimation andclassificationofreviewsalongwithmultipleindependentwordswithsimilarmeaninginparallel. The experimental results show the classification accuracy of the proposed technique tobemoreaccuratecomparedtothe existinglogistic regressionandnaïvebayesclassifiers.