Publication:
Formulation of an AI-Based Call Analytics Model for Analysing Mixed-Language Customer Calls

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dc.citedby0
dc.contributor.authorDewi D.A.en_US
dc.contributor.authorSalleh F.H.M.en_US
dc.contributor.authorNazeri S.en_US
dc.contributor.authorAzmi N.N.en_US
dc.contributor.authorid55012068200en_US
dc.contributor.authorid26423229000en_US
dc.contributor.authorid55372569700en_US
dc.contributor.authorid56337544500en_US
dc.date.accessioned2025-03-03T07:46:22Z
dc.date.available2025-03-03T07:46:22Z
dc.date.issued2024
dc.description.abstractIn this modern age, customer service management is often considered to require more digital communications, such as email and the web. Phone calls are the most reliable way for customers to request services and information directly at an instantaneous rate. However, for companies operating in Malaysia, most of the phone calls received are in Bahasa Malaysia mixed with English (we call this ?Manglish?), whereas the existing software focuses on a single language. This research proposes a model to transform the audio of customer calls into useful information such as topics, complaints, service requests, inquiries, sentiments (positive, negative, neutral), and emotions. This research focuses on data collected from the electricity supply industry. The sentiment analysis experiment, conducted using a deep learning model, generates an accuracy of 92.86% for the Malay language and 75% for English. The results of the experiment reveal a word error count of 37.18% for the Malay language, 45.68% for English, and 59.65% for Manglish. For the topic classification experiment, deep learning (Neural Network), achieved 45.45% accuracy for Malay and 55.56% accuracy for English. An emotion recognition experiment recorded an accuracy of 89.58%. Some improvements to the existing model are also listed in this study. ? The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.en_US
dc.description.natureFinalen_US
dc.identifier.doi10.1007/978-981-97-2977-7_42
dc.identifier.epage691
dc.identifier.scopus2-s2.0-85204360622
dc.identifier.spage675
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85204360622&doi=10.1007%2f978-981-97-2977-7_42&partnerID=40&md5=7bbc579a82c8721c02f83fb0d7ecbcb2
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/36986
dc.identifier.volume1199 LNEE
dc.pagecount16
dc.publisherSpringer Science and Business Media Deutschland GmbHen_US
dc.sourceScopus
dc.sourcetitleLecture Notes in Electrical Engineering
dc.subjectDeep neural networks
dc.subjectElectric utilities
dc.subjectEmotion Recognition
dc.subjectAnalytic modeling
dc.subjectCustomer service management
dc.subjectElectricity supply
dc.subjectEnergy
dc.subjectMalay languages
dc.subjectMalaysia
dc.subjectModern ages
dc.subjectPhone calls
dc.subjectSentiment analysis
dc.subjectSupply industry
dc.subjectSales
dc.titleFormulation of an AI-Based Call Analytics Model for Analysing Mixed-Language Customer Callsen_US
dc.typeConference paperen_US
dspace.entity.typePublication
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