NORMA eResearch @NCI Library

Automatic Ticket Assignment using Machine Learning and Deep Learning Techniques

Shah, Sejal (2020) Automatic Ticket Assignment using Machine Learning and Deep Learning Techniques. Masters thesis, Dublin, National College of Ireland.

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Abstract

One of the important factors of Information Technology Service Industries is to provide user satisfaction and better customer service while saving cost. To achieve the said objective industries often opt for Ticketing system to handle unforeseen service interrupting events. These unforeseen events are also termed as ’incident’. Incident management processes provide quick fixes or workarounds to solve the incident and ensure little or no business impact. Traditionally, the incidents have been manually assigned to the concern teams which has sometimes resulted in human errors, resource consumption, higher response and resolution times and ultimately poor customer service. This research project focuses on automated analysis and allocation of ’incidents’ to appropriate teams using Natural Language Processing and Machine Learning techniques. The data set used in this research contains information in multiple languages and the data has been translated into English for better understanding of the data indicating that the solution to this problem could be obtained using a combination of various data pre-processing, Language Translation and Classification techniques. High class imbalance has been identified in the data set and it has been solved using combination of under-sampling and over-sampling techniques. To classify the incident tickets multiple algorithm namely K-Nearest Neighbours(KNN), Support Vector Machine(SVM), Random Forest(RF),Artificial Neural Network(ANN),Bidirectional Long Short Term Memory(BLSTM) have been trained and hyper-parameter tuning has been performed using different cross validation techniques. Furthermore, confidence interval and k-fold cross validation technique have been used to validate the scores. The ANN model has obtained 93.6% and 95.1% confidence interval at 95% alpha while accuracy score is 95% on the test data.
Keywords-Ticketing system, Natural Language Processing, KNN, SVM, RF, ANN, BLSTM

Item Type: Thesis (Masters)
Subjects: Q Science > QA Mathematics > Electronic computers. Computer science
T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science

Q Science > QA Mathematics > Computer software
T Technology > T Technology (General) > Information Technology > Computer software
Divisions: School of Computing > Master of Science in Data Analytics
Depositing User: Dan English
Date Deposited: 21 Jan 2021 10:32
Last Modified: 21 Jan 2021 10:32
URI: http://norma.ncirl.ie/id/eprint/4416

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