Statistical Validation of ACO-KNN Algorithm for Sentiment Analysis

Authors

  • Siti Rohaidah Ahmad
  • Azuraliza Abu Bakar
  • Mohd Ridzwan Yaakub
  • Nurhafizah Moziyana Mohd Yusop

Keywords:

Feature Selection, Sentiment Analysis, Statistical Analysis, Ant Colony Optimization,

Abstract

This research paper aims to propose a hybrid of ant colony optimization (ACO) and k-nearest neighbour (KNN) algorithms as feature selections for selecting and choosing relevant features from customer review datasets. Information gain (IG), genetic algorithm (GA), and rough set attribute reduction (RSAR) were used as baseline algorithms in a performance comparison with the proposed algorithm. This paper will also discuss the significance test, which was used to evaluate the performance differences between the ACO-KNN, the IG-GA, and the IG-RSAR algorithms. The dependency relation algorithm was used to identify actual features commented by customers by linking the dependency relation between product feature and sentiment words in customers sentences. This study evaluated the performance of the ACOKNN algorithm using precision, recall, and F-score, which was validated using the parametric statistical significance tests. The evaluation process has statistically proven that this ACO-KNN algorithm has been significantly improved compared to the baseline algorithms. In addition, the experimental results have proven that the ACO-KNN can be used as a feature selection technique in sentiment analysis to obtain quality, optimal feature subset that can represent the actual data in customer review data.

Downloads

Published

2017-09-15

How to Cite

Ahmad, S. R., Abu Bakar, A., Yaakub, M. R., & Mohd Yusop, N. M. (2017). Statistical Validation of ACO-KNN Algorithm for Sentiment Analysis. Journal of Telecommunication, Electronic and Computer Engineering (JTEC), 9(2-11), 165–170. Retrieved from https://jtec.utem.edu.my/jtec/article/view/2757

Issue

Vol. 9 No. 2-11: The Challenge of Computer and Engineering in Building Sustainable Service Systems I

Section

Articles

License

TRANSFER OF COPYRIGHT AGREEMENT

The manuscript is herewith submitted for publication in the Journal of Telecommunication, Electronic and Computer Engineering (JTEC). It has not been published before, and it is not under consideration for publication in any other journals. It contains no material that is scandalous, obscene, libelous or otherwise contrary to law. When the manuscript is accepted for publication, I, as the author, hereby agree to transfer to JTEC, all rights including those pertaining to electronic forms and transmissions, under existing copyright laws, except for the following, which the author(s) specifically retain(s):

  1. All proprietary right other than copyright, such as patent rights
  2. The right to make further copies of all or part of the published article for my use in classroom teaching
  3. The right to reuse all or part of this manuscript in a compilation of my own works or in a textbook of which I am the author; and
  4. The right to make copies of the published work for internal distribution within the institution that employs me

I agree that copies made under these circumstances will continue to carry the copyright notice that appears in the original published work. I agree to inform my co-authors, if any, of the above terms. I certify that I have obtained written permission for the use of text, tables, and/or illustrations from any copyrighted source(s), and I agree to supply such written permission(s) to JTEC upon request.