Mahalanobis Distance-the Ultimate Measure for Sentiment Analysis
Journal: The International Arab Journal of Information Technology (Vol.13, No. 2)Publication Date: 2016-03-01
Authors : Valarmathi Balasubramanian; Srinivasa Gupta Nagarajan; Palanisamy Veerappagoundar;
Page : 252-257
Keywords : Sentiment analysis; MD; opinion mining; machine learning algorithms; hybrid classifier.;
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.
Other Latest Articles
- The Implication of Customer Relationship in Office Facility Management of Upstream Oil and Gas Business
- Texts Semantic Similarity Detection Based Graph Approach
- Resource Provisioning for various network applications in adhoc networks
- Adaptive Mobile Learning Framework Based On IRT Theory
- Adaptive Mobile Learning Framework Based On IRT Theory
Last modified: 2019-11-13 19:30:26