International Journal of Computer Science & Network Security

International Journal of Computer Science & Network Security (국제컴퓨터통신보호논문지학회)
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Academic Registration Text Classification Using Machine Learning

  • Received : 2021.12.05
  • Published : 2022.01.30
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Abstract

Natural language processing (NLP) is utilized to understand a natural text. Text analysis systems use natural language algorithms to find the meaning of large amounts of text. Text classification represents a basic task of NLP with a wide range of applications such as topic labeling, sentiment analysis, spam detection, and intent detection. The algorithm can transform user's unstructured thoughts into more structured data. In this work, a text classifier has been developed that uses academic admission and registration texts as input, analyzes its content, and then automatically assigns relevant tags such as admission, graduate school, and registration. In this work, the well-known algorithms support vector machine SVM and K-nearest neighbor (kNN) algorithms are used to develop the above-mentioned classifier. The obtained results showed that the SVM classifier outperformed the kNN classifier with an overall accuracy of 98.9%. in addition, the mean absolute error of SVM was 0.0064 while it was 0.0098 for kNN classifier. Based on the obtained results, the SVM is used to implement the academic text classification in this work.

Keywords

Acknowledgement

I want to thank my university, "University of Ha'il" for providing us with all the needed facilities to complete this master's degree. A special thanks to Gharbi Alshammari for his continuous guide and support. I want to acknowledge and thank my department for allowing me to conduct my research and providing any assistance requested. Finally, I would like to thank all my friends and colleagues who have helped me on this project. Their enthusiasm and willingness to provide feedback made completing this study an enjoyable experience.

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