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Mahalanobis Distance-the Ultimate Measure for Sentiment Analysis

Journal: The International Arab Journal of Information Technology (Vol.13, No. 2)

Publication Date:

Authors : ; ; ;

Page : 252-257

Keywords : Sentiment analysis; MD; opinion mining; machine learning algorithms; hybrid classifier.;

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Abstract

In this paper, Mahalanobis Distance (MD) has been proposed as a measure to classify the sentiment expressed in a review document as either positive or negative. A new method for representing the text documents using Representative Terms (RT) has been used. The new way of representing text documents using few representative dimensions is relatively a new concept, which is successfully demonstrated in this paper. The MD based classifier performed with 70.8% of accuracy for the experiments carried out using the benchmark dataset containing 25000 movie reviews. The hybrid of MD based Classifier (MDC) and Multi Layer Perceptron (MLP) resulted in a 98.8% of classification accuracy, which is the highest ever reported accuracy for a dataset containing 25000 reviews.

Last modified: 2019-11-13 19:30:26