Modeling Sentiment Polarity in Support Ticket Data for Predicting Cloud Service Subscription Renewal

Gajananan, Kugamoorthy; Loyola, P.; Katsuno, Yasuharu; Munawar, Asim; Trent, Scott; Satoh, Fumiko

doi:10.1109/scc.2018.00014

Cited by 2 publications

(4 citation statements)

References 28 publications

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“…Respective the topic of customer sentiment/emotion prediction, we see a great potential for further research work. Knowing customer sentiment has big economical potential [30] either by helping customer requests not to escalate [5], by helping to prioritize requests [6] or by helping to predict if customers would renew their subscriptions [31]. But, multiple other use cases for knowing customer sentiment can be thought.…”

Section: Discussionmentioning

confidence: 99%

“…Qamili, Shabani [3] report low accuracy (<45%) due to the difficulties in accumulating labeled data for sentiment prediction. Gajananan, Loyola [31] report high accuracy for their subscription renewal prediction, but do not directly predict customer sentiment. Instead, they only use sentiment polarity as a parameter of their model.…”

Section: Sentiment Predictionmentioning

confidence: 99%

“…Werner, Li [30] x x x Gajananan, Loyola [31] (x) x Gupta, Asadullah [1] x x Han, Goh [32] x Koehler, Fux [33] x x Lyubinets, Boiko [34] (x) x Meng, Xu [35])…”

Section: Sent Pred Othermentioning

confidence: 99%

“…The predicted sentiment of a customer can then be used for a wide scope of application: Gajananan, Loyola [31] developed an artifact predicting customer sentiment from support ticket data for predicting the probability if a customer would renew his subscription at a cloud business service; Werner, Li [30] use customer sentiment prediction to predict the risk of support escalation; and Qamili, Shabani [3] want to use customer sentiment prediction for internal evaluation of the support system.…”

Section: Sentiment Predictionmentioning

confidence: 99%

See 3 more Smart Citations

Improving Support Ticket Systems Using Machine Learning: A Literature Review

Fuchs¹,

Drieschner²,

Wittges³

2022

Proceedings of the Annual Hawaii International Conference on System Sciences

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Processing customer support requests via a support ticket system is a key-element for companies to provide support to their customers in an organized and professional way. However, distributing and processing such tickets is much work, increasing the cost for the support providing company and stretching the resolution time. The advancing potential of Machine Learning has led to the goal of automating those support ticket systems. Against this background, we conducted a Literature Review aiming at determining the present state-of-the-art technology in the field of automated support ticket systems. We provide an overview about present trends and topics discussed in this field. During the Literature Review, we found creating an automated incident management tool being the majority topic in the field followed by request escalation and customer sentiment prediction and identified Random Forrest and Support Vector Machine as best performing algorithms for classification in the field.

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Section: Discussionmentioning

confidence: 99%

Section: Sentiment Predictionmentioning

confidence: 99%

“…Werner, Li [30] x x x Gajananan, Loyola [31] (x) x Gupta, Asadullah [1] x x Han, Goh [32] x Koehler, Fux [33] x x Lyubinets, Boiko [34] (x) x Meng, Xu [35])…”

Section: Sent Pred Othermentioning

confidence: 99%