A Comparison of Different Topic Modeling Methods through a Real Case Study of Italian Customer Care

The paper deals with the analysis of conversation transcriptions between customers and agents in a call center of a customer care service. The objective is to support the analysis of text transcription of human-to-human conversations, to obtain reports on customer problems and complaints, and on the...

Full description

Saved in:
Bibliographic Details
Published in:Algorithms 2023-02, Vol.16 (2), p.94
Main Authors: Papadia, Gabriele, Pacella, Massimo, Perrone, Massimiliano, Giliberti, Vincenzo
Format: Article
Language:English
Subjects:
Algorithms
Analysis
Call centers
Case studies
Computational linguistics
contextualized topic models
Customer relations
Customer satisfaction
Customer services
Customers
Data mining
Datasets
Dirichlet problem
Italian language
Italian natural language
Language processing
latent dirichlet allocation
Literature reviews
Methods
Modelling
Natural language
Natural language interfaces
neural-ProdLDA
non-negative matrix factorization
topic modeling
User satisfaction
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:The paper deals with the analysis of conversation transcriptions between customers and agents in a call center of a customer care service. The objective is to support the analysis of text transcription of human-to-human conversations, to obtain reports on customer problems and complaints, and on the way an agent has solved them. The aim is to provide customer care service with a high level of efficiency and user satisfaction. To this aim, topic modeling is considered since it facilitates insightful analysis from large documents and datasets, such as a summarization of the main topics and topic characteristics. This paper presents a performance comparison of four topic modeling algorithms: (i) Latent Dirichlet Allocation (LDA); (ii) Non-negative Matrix Factorization (NMF); (iii) Neural-ProdLDA (Neural LDA) and Contextualized Topic Models (CTM). The comparison study is based on a database containing real conversation transcriptions in Italian Natural Language. Experimental results and different topic evaluation metrics are analyzed in this paper to determine the most suitable model for the case study. The gained knowledge can be exploited by practitioners to identify the optimal strategy and to perform and evaluate topic modeling on Italian natural language transcriptions of human-to-human conversations. This work can be an asset for grounding applications of topic modeling and can be inspiring for similar case studies in the domain of customer care quality.
ISSN:1999-4893
1999-4893
DOI:10.3390/a16020094