Please use this identifier to cite or link to this item: https://rda.sliit.lk/handle/123456789/3164
Title: Anonymo: Automatic Response and Analysis of Anonymous Caller Complaints
Authors: Azhar, A
Maweekumbura, S
Gunathilake, R
Maddumaarachchi, T
Karunasena, A
Nadeeshani, M
Keywords: classification
Conversational AI
Machine Learning
Natural Language Processing
Issue Date: 17-Aug-2022
Publisher: IEEE Computer Society
Citation: A. Azhar, S. Maweekumbura, R. Gunathilake, T. Maddumaarachchi, A. Karunasena and M. Nadeeshani, "Anonymo: Automatic Response and Analysis of Anonymous Caller Complaints," 2022 IEEE Symposium on Wireless Technology & Applications (ISWTA), Kuala Lumpur, Malaysia, 2022, pp. 110-115, doi: 10.1109/ISWTA55313.2022.9942736.
Series/Report no.: IEEE Symposium on Wireless Technology and Applications, ISWTAVolume 2022-August,;Pages 110 - 115
Abstract: Customers are considered as the most valued asset in any business organization. Therefore, attending especially to negative feedback provided by customer in form of complaints is important for an organization to identify areas to improve and retain customers. To quickly respond to customer complaints many business organizations have made hotlines available. Such caller hotlines are dedicated for the purpose of receiving complaints or allowing whistleblowers to reveal information. Due to the fear of being identified, there is a hesitancy in the public to use these hotlines. From the perspective of the organizations when a customer complaint is received it is required to evaluate the validity of the call made to hotlines. Furthermore, when complaints are made, it is required to handle them efficiently by transferring them to relevant departments and prioritize complaints This research proposes 'Anonymo', a system to handle customer complaints in a secure and an efficient manner. To do so, the system analyses the complaints obtained by a caller and provides the end users with the appropriate responses and output, that includes the following: i. Conversational AI agent to respond to callers, ii. Wanted and unwanted call classification, iii. Department-based Complaint classification, iv. Caller Emotion detection and caller complaint analysis while establishing the caller's anonymity. An accuracy of 88.26% was obtained for identification of wanted complaints using SVM algorithm, an accuracy of 85% was obtained for department-based classification using SVM algorithm and 67% accuracy was obtained for emotion analysis by LSTM algorithm
URI: https://rda.sliit.lk/handle/123456789/3164
ISBN: 978-166548482-4
Appears in Collections:Department of Information Technology

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