Improve opinion mining by combining gray wolf algorithm and support vector machine

Document Type : Original Article

Author

Department of Industrial Management, Faculty of Management, Islamic Azad University, South Tehran Branch, Tehran, Iran.

Abstract

The advent of the web and its continuous growth have created a tremendous amount of user-generated information. Valuable subjective information is easy to find. Especially on social networks and e-commerce platforms that contain essential information. As a result, the field of belief mining has attracted considerable attention in recent years. New research papers are published every day in which various AI techniques are applied to various tasks and applications related to mining opinion. In this article, a new approach based on the support vector machine technique and the gray wolf optimization algorithm is proposed to improve the opinion mining process. Such that the gray wolf algorithm is used to determine the practical features in the belief mining process and enhances the performance of the support vector machine. This research showed that the proposed system could increase the accuracy and cover the error of the backup vector technique by selecting practical features. The proposed approach has been evaluated using three criteria: accuracy, recall, and precision. Precision for the first and second classes is 0.68 and 0.92%, respectively, and recall for the first and second classes equals 0.94 and 0.63% respectively, and accuracy was 0.77%. The results indicate that the proposed system of this research achieved favorable results in both categories.

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References

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Soft Computing Journal
Volume 13, Issue 1 - Serial Number 25
September 2024
Pages 96-107

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History

  • Receive Date: 25 December 2022
  • Revise Date: 05 September 2023
  • Accept Date: 22 September 2023

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