Research Article Open Access

Sentiment Analysis: Comparative Study between GSVM and KNN

Hany Mohamed1, Ayman Atia2 and Mostafa-Sami M. Mostafa3
  • 1 Faculty of Computer Science, Helwan University, Egypt
  • 2 Faculty of Computer Science, Misr International University, Helwan University, HCI-LAB, Egypt
  • 3 Faculty of Computer Science, Helwan University, HCI-LAB, Egypt

Abstract

Sentiment classification aims detecting general opinion of users in social media towards business products or daily life events. The classification tells whether sentiment is positive or negative. Techniques of sentiment classification are categorized into lexical analysis and machine learning techniques. In this paper, we propose a comparative study between SVM applied genetics (GSVM) against KNN algorithm in terms of speed and accuracy. We present also an experimental study of sentiment classification on different domains movie reviews, financial and amazon toys products. The experimental results shows that GSVM achieves a classification accuracy of 92% and KNN achieves 87% on movie reviews dataset. For classification speed, KNN shows a remarkable improvement (above 10% improvement) in comparison with GSVM.

American Journal of Applied Sciences
Volume 15 No. 6, 2018, 339-345

DOI: https://doi.org/10.3844/ajassp.2018.339.345

Submitted On: 5 April 2018 Published On: 1 August 2018

How to Cite: Mohamed, H., Atia, A. & Mostafa, M. M. (2018). Sentiment Analysis: Comparative Study between GSVM and KNN. American Journal of Applied Sciences, 15(6), 339-345. https://doi.org/10.3844/ajassp.2018.339.345

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Keywords

  • Sentiment Classification
  • SVM
  • KNN
  • NLP
  • GENETICS