Text Mining Approaches Oriented on Customer Care Efficiency

Alessandro Massaro, Nicola Magaletti, Gabriele Cosoli, Vito Giardinelli and Angelo Leogrande

MPRA Paper from University Library of Munich, Germany

Abstract: In the proposed work is performed a text classification for a chatbot application used by a company working in assistance services of automatic warehouses. industries. Specifically, text mining technique is adopted for the classification of questions and answers. Business Process Modeling Notation (BPMN) models describe the passage from “AS-IS” to “TO BE” in the context of the analyzed industry, by focusing the attention mainly on customer and technical support services where chatbot is adopted. A two-step process model is used to connect technological improvements and relationship marketing in chatbot assistance: the first step provides the hierarchical clustering able to classify questions and answers through Latent Dirichlet Allocation -LDA- algorithm, and the second one executes the Tag Cloud representing the visual representation of more frequent words contained in the experimental dataset. Tag cloud is used to show the critical issues that customers find in the usage of the proposed service. By considering an initial dataset, 24 hierarchical clusters are found representing the preliminary combination of the couple’s question-answer. The proposed approach is suitable to automatically construct a combination of chatbot questions and appropriate answers in intelligent systems.

Keywords: Chatbot; Speech Recognition; Natural Language Processing-NLP; Hierarchical Clustering; Business Process Management and Notation-BPMN. (search for similar items in EconPapers)
JEL-codes: O3 O30 O31 O32 O33 O34 (search for similar items in EconPapers)
Date: 2022-03-05
New Economics Papers: this item is included in nep-big
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Working Paper: Text Mining Approaches Oriented on Customer Care Efficiency (2022)
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