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Automated categorization of support tickets using machine learning

Andréason, Hanna LU and Nilsson, Christopher (2018) In LU-CS-EX 2018-14 EDAM05 20181
Department of Computer Science
Abstract
Customer Support is usually an expensive service for a lot of companies.
At Telavox, a majority of the support tickets are sent via e-mail. A support
ticket is sent by a customer, containing a request for support. Support tickets
from customers could reveal valuable information. Additional insights can
be given by labelling the tickets. Today, employees at the customer support
department at Telavox assign labels to the tickets manually. In this thesis, we
explore whether it is possible to automatically classify support tickets from a
predetermined set of labels based on their textual representation. We explore
different data representations, classification algorithms, clustering algorithms
and neural nets to find the best... (More)
Customer Support is usually an expensive service for a lot of companies.
At Telavox, a majority of the support tickets are sent via e-mail. A support
ticket is sent by a customer, containing a request for support. Support tickets
from customers could reveal valuable information. Additional insights can
be given by labelling the tickets. Today, employees at the customer support
department at Telavox assign labels to the tickets manually. In this thesis, we
explore whether it is possible to automatically classify support tickets from a
predetermined set of labels based on their textual representation. We explore
different data representations, classification algorithms, clustering algorithms
and neural nets to find the best solution. We found that a ridge regression
classifier gave the best result. We also carried out a qualitative analysis of the
corpus and found that it is not always easy to classify tickets by looking only
at the text. Finally, we develop an application that could be used to predict
what label is best suited for a given support ticket. Using the probability, our
model can rank the predictions. The correct label is presented in the three first
predicted labels 91.6% of the time. (Less)
Please use this url to cite or link to this publication:
author
Andréason, Hanna LU and Nilsson, Christopher
supervisor
organization
course
EDAM05 20181
year
type
H2 - Master's Degree (Two Years)
subject
keywords
Classification, Clustering, Scikit-learn, Text Categorization, support tickets
publication/series
LU-CS-EX 2018-14
report number
LU-CS-EX 2018-14
ISSN
1650-2884
language
English
id
8962734
date added to LUP
2018-12-13 15:35:02
date last changed
2018-12-13 15:35:02
@misc{8962734,
  abstract     = {{Customer Support is usually an expensive service for a lot of companies.
At Telavox, a majority of the support tickets are sent via e-mail. A support
ticket is sent by a customer, containing a request for support. Support tickets
from customers could reveal valuable information. Additional insights can
be given by labelling the tickets. Today, employees at the customer support
department at Telavox assign labels to the tickets manually. In this thesis, we
explore whether it is possible to automatically classify support tickets from a
predetermined set of labels based on their textual representation. We explore
different data representations, classification algorithms, clustering algorithms
and neural nets to find the best solution. We found that a ridge regression
classifier gave the best result. We also carried out a qualitative analysis of the
corpus and found that it is not always easy to classify tickets by looking only
at the text. Finally, we develop an application that could be used to predict
what label is best suited for a given support ticket. Using the probability, our
model can rank the predictions. The correct label is presented in the three first
predicted labels 91.6% of the time.}},
  author       = {{Andréason, Hanna and Nilsson, Christopher}},
  issn         = {{1650-2884}},
  language     = {{eng}},
  note         = {{Student Paper}},
  series       = {{LU-CS-EX 2018-14}},
  title        = {{Automated categorization of support tickets using machine learning}},
  year         = {{2018}},
}