Identification of Telecom Volatile Customers Using a Particle Swarm Optimized K-Means Clustering on Their Personality Traits Analysis

Abdelsadeq Khamis Elfergany and Ammar Adl
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Abdelsadeq Khamis Elfergany: Faculty of Computers and Information, Beni-Suef University, Beni-Suef, Egypt
Ammar Adl: Faculty of Computers and Information, Beni-Suef University, Beni-Suef, Egypt

International Journal of Service Science, Management, Engineering, and Technology (IJSSMET), 2020, vol. 11, issue 2, 1-15

Abstract: This research uses the telecom customers personality traits (extraversion, agreeableness, and neuroticism) to identify the volatile customers that always use the negative word of mouth (NWOM) in communications with others. Hence, a combination of text analysis and a personality analysis tool has been used to determine the customers personality factors from their chatting textual data, A particle swarm optimized k-means was used in the clustering process. The results provide an overview on how a chatbot conversation text represent the customer behavior. Optimizing the k-means cluster using partial swarm achieves a higher accuracy than using the traditional clustering technique.

Date: 2020
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Persistent link: https://EconPapers.repec.org/RePEc:igg:jssmet:v:11:y:2020:i:2:p:1-15

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International Journal of Service Science, Management, Engineering, and Technology (IJSSMET) is currently edited by Ahmad Taher Azar

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