A Customer Complaint Analysis Tool for Mobile Network Operators

Kalyoncu, Feyzullah; Zeydan, Engin; Yigit, Ibrahim Onuralp; Yildirim, Ahmet

doi:10.1109/asonam.2018.8508289

Cited by 10 publications

(5 citation statements)

References 4 publications

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“…Then the literature review was conducted at the DS-I stage to analyze the solutions for a similar problem from previous studies. Various researches have explored the call center records with multiple objectives, including building Automatic Speech Recognition [2,3] and Speech Act Detection [4], analysis of emotions and customer satisfaction [5,6], complaint distribution and analysis [7,8], and others [9]. Table 1 resumes the methods of speech-transcribing, data modeling and classification model that are used in previous studies.…”

Section: Related Workmentioning

confidence: 99%

“…Various studies have been performed using artificial intelligence technology to process customer complaint data to increase the company's business [2][3][4][5][6][7][8][9]. Machine learning is an applied branch of artificial intelligence that focuses on developing a system that can replace the capability of humans in the decision making process through the learning process.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Deep Learning-Based Complaint Classification for Indonesia Telecommunication Company's Call Center

Lukitasari¹,

Hidayat²

2020

Proceedings of the Proceedings of the 7th Mathematics, Science, and Computer Science Education International Seminar, MSCEIS 20

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The preliminary research was held to utilize the call center conversations records from a broadband telecommunications company in Indonesia. There is a need from the company to classify customer's complaints automatically by a system to minimize human errors and at once streamline the business processes and resources. Natural Language Processing (NLP), as an integral part of artificial intelligence (AI), empower machines to understand human languages for performing beneficial tasks. The growth of deep learning is the main driver behind NLP for performing various practical applications and business, therefore deep learning is expected to overcome the problems encountered. This paper explains the methods used in designing the classification systems based on deep learning. A literature review is conducted to find the proper algorithm used in classifying problems. Furthermore, it also explains the stages performed in preparing the data and building the system model. From experiments conducted, it can be stated that the RNN algorithm can be used in the classification of customer complaints with the results shown by the accuracy value of the model.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Deep Learning-Based Complaint Classification for Indonesia Telecommunication Company's Call Center

Lukitasari¹,

Hidayat²

2020

Proceedings of the Proceedings of the 7th Mathematics, Science, and Computer Science Education International Seminar, MSCEIS 20

View full text Add to dashboard Cite

show abstract

“…These techniques have been applied in complaint analysis by Fauzan and Khodra (2014), although with modest results. Kalyoncu et al (2018) approach customer complaint analysis from a topic modeling perspective, using techniques such as Latent Dirichlet Allocation (LDA) (Blei et al, 2003). This work is not so much focused on automatically processing complaints, but instead on providing a visualization tool for mobile network operators.…”

Section: Related Workmentioning

confidence: 99%

Complaint Analysis and Classification for Economic and Food Safety

Filgueiras¹,

Barbosa²,

Rocha³

et al. 2019

Proceedings of the Second Workshop on Economics and Natural Language Processing

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Governmental institutions are employing artificial intelligence techniques to deal with their specific problems and exploit their huge amounts of both structured and unstructured information. In particular, natural language processing and machine learning techniques are being used to process citizen feedback. In this paper, we report on the use of such techniques for analyzing and classifying complaints, in the context of the Portuguese Economic and Food Safety Authority. Grounded in its operational process, we address three different classification problems: target economic activity, implied infraction severity level, and institutional competence. We show promising results obtained using feature-based approaches and traditional classifiers, with accuracy scores above 70%, and analyze the shortcomings of our current results and avenues for further improvement, taking into account the intended use of our classifiers in helping human officers to cope with thousands of yearly complaints.

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“…Jin et al presented the potential network problems identification method based on customer tickets or LRT, a smartphone application for collecting large-scale feedback from mobile customers [4,5]. Feyzullah et al designed a LDA based topic model to analyze the customer complaint and showed top topic distributions [6]. Considering the inaccuracy of user complaints, Li Yan et al created a rural distribution network fault location algorithm based on fault complaint information [7].…”

Section: Related Workmentioning

confidence: 99%

Smart Prediction of the Complaint Hotspot Problem in Mobile Network

Zhu

Zhao

Wang

et al. 2019

Proceedings of the 2019 Workshop on Network Meets AI &Amp; ML - NetAI'19

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In mobile network, a complaint hotspot problem often affects even thousands of users' service and leads to significant economic losses and bulk complaints. In this paper, we propose an approach to predict a customer complaint based on real-time user signalling data. Through analyzing the network and user sevice procedure, 30 key data fields related to user experience have been extracted in XDR data collected from the S1 interface. Furthermore, we augment these basic features with derived features for user experience evaluation, such as one-hot features, statistical features and differential features. Considering the problems of unbalanced data, we use LightGBM as our prediction model. LightGBM has strong generalization ability and was designed to handle unbalanced data. Experiments we conducted prove the effectiveness and efficiency of this proposal. This approach has been deployed for daily routine to locate the hot complaint problem scope as well as to report affected users and area.

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A Customer Complaint Analysis Tool for Mobile Network Operators

Cited by 10 publications

References 4 publications

Deep Learning-Based Complaint Classification for Indonesia Telecommunication Company's Call Center

Deep Learning-Based Complaint Classification for Indonesia Telecommunication Company's Call Center

Complaint Analysis and Classification for Economic and Food Safety

Smart Prediction of the Complaint Hotspot Problem in Mobile Network

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