Classification of Telephone Subscriber Errors Based on Text Messages in Vietnamese Language
Phan Thi Ha1, Phuong Nguyen2
1Dr. Phan Thi Ha, Department of Information Technology, Posts and Telecommunications Institute of Technology, Hanoi, Vietnam.
2Dr. Phuong Nguyen, Department of Information Technology, Posts and Telecommunications Institute of Technology, Hanoi, Vietnam.
Manuscript received on 15 August 2018 | Revised Manuscript received on 27 August 2018 | Manuscript published on 30 November 2018 | PP: 5-8 | Volume-7 Issue-11, August 2018 | Retrieval Number: K25160871118/18©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: This article describes a method for automatically classifying telecommunications subscriber errors based on text messages, using a machine learning method Support Vector Machine (SVM). The SVM method trains and tests on a set of data obtained from the text messages in Vietnamese of the actual line workers sending to the service operation centers. The results show that the proposed classification method using the SVM gives high accuracy and can be applied in practice.
Keyword: Text Classification, Natural Language Processing, Learning Support Vector Machine.
Scope of the Article: Classification