Sentiment Analysis: Comparative Study between GSVM and KNN
- 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
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.
Copyright: © 2018 Hany Mohamed, Ayman Atia and Mostafa-Sami M. Mostafa. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
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- Sentiment Classification