Question Classification for Helpdesk Support Forum Using Support Vector Machine and Naïve Bayes Algorithm

IF 1.3 Q4 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Noor Aklima Harun, S. Huspi, Noorminshah A. Iahad
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引用次数: 0

Abstract

The helpdesk support system is now essential in ensuring the journey of support services runs more systematically. One of the elements that contribute to the non-uniformity of the question data in the Helpdesk Support System is the diversity of services and users. Most questions asked in the system are in various forms and sentence styles but usually offer the same meaning making its hard for automation of the question classification process. This has led to problems such as the tickets being forwarded to the wrong resolver group, causing the ticket transfer process to take longer response. The key findings in the exploration results revealed that tickets with a high number of transfer transactions take longer to complete than tickets compared to no transfer transaction. Thus, this research aims to develop an automated question classification model for the Helpdesk Support System by applying supervised machine learning methods: Naïve Bayes (NB) and Support Vector Machine (SVM). The domain will use a readily available dataset from the IT Unit. The results using these techniques are then evaluated using confusion matrix and classification report evaluation, including precision, recall, and F1-Measure measurement. The outcomes showed that the SVM algorithm and TF-IDF feature extraction outperformed in terms of accuracy score compared to the NB algorithm. It is expected that this study will have a significant impact on the productivity of team technical and system owners in dealing with the increasing number of comments, feedback, and complaints presented by end-users.
基于支持向量机和Naïve贝叶斯算法的Helpdesk支持论坛问题分类
帮助台支持系统现在对于确保支持服务的旅程更系统地运行至关重要。导致Helpdesk支持系统中问题数据不一致的因素之一是服务和用户的多样性。系统中提出的大多数问题都有不同的形式和句子风格,但通常提供相同的含义,这使得问题分类过程的自动化变得困难。这导致了一些问题,例如将票据转发到错误的解析器组,从而导致票据传输过程需要更长的响应时间。勘探结果的主要发现表明,与没有转让交易的票相比,有大量转让交易的票需要更长的时间才能完成。因此,本研究旨在应用监督机器学习方法:Naïve贝叶斯(NB)和支持向量机(SVM),为Helpdesk支持系统开发一个自动问题分类模型。域将使用来自IT单元的现成可用的数据集。然后使用混淆矩阵和分类报告评估来评估使用这些技术的结果,包括精度、召回率和F1-Measure测量。结果表明,SVM算法和TF-IDF特征提取在准确率得分上优于NB算法。预计这项研究将对团队技术和系统所有者在处理由最终用户提出的越来越多的评论、反馈和投诉方面的生产力产生重大影响。
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来源期刊
CiteScore
3.20
自引率
20.00%
发文量
0
审稿时长
4.3 months
期刊介绍: The primary aim of the International Journal of Innovative Computing, Information and Control (IJICIC) is to publish high-quality papers of new developments and trends, novel techniques and approaches, innovative methodologies and technologies on the theory and applications of intelligent systems, information and control. The IJICIC is a peer-reviewed English language journal and is published bimonthly
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