Formulation of an AI-Based Call Analytics Model for Analysing Mixed-Language Customer Calls
In 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 Malays...
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2025
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my.uniten.dspace-369862025-03-03T15:46:22Z Formulation of an AI-Based Call Analytics Model for Analysing Mixed-Language Customer Calls Dewi D.A. Salleh F.H.M. Nazeri S. Azmi N.N. 55012068200 26423229000 55372569700 56337544500 Deep neural networks Electric utilities Emotion Recognition Analytic modeling Customer service management Electricity supply Energy Malay languages Malaysia Modern ages Phone calls Sentiment analysis Supply industry Sales In 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. Final 2025-03-03T07:46:22Z 2025-03-03T07:46:22Z 2024 Conference paper 10.1007/978-981-97-2977-7_42 2-s2.0-85204360622 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85204360622&doi=10.1007%2f978-981-97-2977-7_42&partnerID=40&md5=7bbc579a82c8721c02f83fb0d7ecbcb2 https://irepository.uniten.edu.my/handle/123456789/36986 1199 LNEE 675 691 Springer Science and Business Media Deutschland GmbH Scopus |
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Deep neural networks Electric utilities Emotion Recognition Analytic modeling Customer service management Electricity supply Energy Malay languages Malaysia Modern ages Phone calls Sentiment analysis Supply industry Sales |
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Deep neural networks Electric utilities Emotion Recognition Analytic modeling Customer service management Electricity supply Energy Malay languages Malaysia Modern ages Phone calls Sentiment analysis Supply industry Sales Dewi D.A. Salleh F.H.M. Nazeri S. Azmi N.N. Formulation of an AI-Based Call Analytics Model for Analysing Mixed-Language Customer Calls |
| description |
In 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. |
| author2 |
55012068200 |
| author_facet |
55012068200 Dewi D.A. Salleh F.H.M. Nazeri S. Azmi N.N. |
| format |
Conference paper |
| author |
Dewi D.A. Salleh F.H.M. Nazeri S. Azmi N.N. |
| author_sort |
Dewi D.A. |
| title |
Formulation of an AI-Based Call Analytics Model for Analysing Mixed-Language Customer Calls |
| title_short |
Formulation of an AI-Based Call Analytics Model for Analysing Mixed-Language Customer Calls |
| title_full |
Formulation of an AI-Based Call Analytics Model for Analysing Mixed-Language Customer Calls |
| title_fullStr |
Formulation of an AI-Based Call Analytics Model for Analysing Mixed-Language Customer Calls |
| title_full_unstemmed |
Formulation of an AI-Based Call Analytics Model for Analysing Mixed-Language Customer Calls |
| title_sort |
formulation of an ai-based call analytics model for analysing mixed-language customer calls |
| publisher |
Springer Science and Business Media Deutschland GmbH |
| publishDate |
2025 |
| _version_ |
1825816081153916928 |
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13.251813 |