Mahalanobis distance-the ultimate measure for sentiment analysis

Abstract EN

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 Mahalanobis Distance 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 review