Understanding Customers’ Transport Services with Topic Clustering and Sentiment Analysis

The recent increase in user interaction with social media has completely changed the way customers communicate their opinions, questions, and concerns to brands. For this reason, many companies have established on the top of their agendas the necessity of analyzing the high amounts of user-generated...

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Autores principales:	Alejandro Moreno, Carlos A. Iglesias
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	Twitter customer service Natural Language Processing topic modeling genetic algorithm local convergence algorithm Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999
Acceso en línea:	https://doaj.org/article/af913e071abd487eb257a01c373fe483
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id	oai:doaj.org-article:af913e071abd487eb257a01c373fe483
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spelling	oai:doaj.org-article:af913e071abd487eb257a01c373fe4832021-11-11T15:14:06ZUnderstanding Customers’ Transport Services with Topic Clustering and Sentiment Analysis10.3390/app1121101692076-3417https://doaj.org/article/af913e071abd487eb257a01c373fe4832021-10-01T00:00:00Zhttps://www.mdpi.com/2076-3417/11/21/10169https://doaj.org/toc/2076-3417The recent increase in user interaction with social media has completely changed the way customers communicate their opinions, questions, and concerns to brands. For this reason, many companies have established on the top of their agendas the necessity of analyzing the high amounts of user-generated content data in social networks. These analyses are helping brands to understand their customers’ experiences as well as for maintaining a competitive advantage in the sector. Due to this fact, this study aims to analyze and characterize the public opinions from the messages posted by Twitter users while addressing customer services. For this purpose, this study carried out a content analysis of a customer service platform. We extracted the general users’ viewpoints and sentiments of each of the discussed topics by using a wide range of techniques, such as topic modeling, document clustering, and opinion mining algorithms. For training these systems and drawing conclusions, a dataset containing tweets from the English-speaking customers addressing the @Uber_Support platform during the year 2020 has been used.Alejandro MorenoCarlos A. IglesiasMDPI AGarticleTwittercustomer serviceNatural Language Processingtopic modelinggenetic algorithmlocal convergence algorithmTechnologyTEngineering (General). Civil engineering (General)TA1-2040Biology (General)QH301-705.5PhysicsQC1-999ChemistryQD1-999ENApplied Sciences, Vol 11, Iss 10169, p 10169 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Twitter customer service Natural Language Processing topic modeling genetic algorithm local convergence algorithm Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999
spellingShingle	Twitter customer service Natural Language Processing topic modeling genetic algorithm local convergence algorithm Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999 Alejandro Moreno Carlos A. Iglesias Understanding Customers’ Transport Services with Topic Clustering and Sentiment Analysis
description	The recent increase in user interaction with social media has completely changed the way customers communicate their opinions, questions, and concerns to brands. For this reason, many companies have established on the top of their agendas the necessity of analyzing the high amounts of user-generated content data in social networks. These analyses are helping brands to understand their customers’ experiences as well as for maintaining a competitive advantage in the sector. Due to this fact, this study aims to analyze and characterize the public opinions from the messages posted by Twitter users while addressing customer services. For this purpose, this study carried out a content analysis of a customer service platform. We extracted the general users’ viewpoints and sentiments of each of the discussed topics by using a wide range of techniques, such as topic modeling, document clustering, and opinion mining algorithms. For training these systems and drawing conclusions, a dataset containing tweets from the English-speaking customers addressing the @Uber_Support platform during the year 2020 has been used.
format	article
author	Alejandro Moreno Carlos A. Iglesias
author_facet	Alejandro Moreno Carlos A. Iglesias
author_sort	Alejandro Moreno
title	Understanding Customers’ Transport Services with Topic Clustering and Sentiment Analysis
title_short	Understanding Customers’ Transport Services with Topic Clustering and Sentiment Analysis
title_full	Understanding Customers’ Transport Services with Topic Clustering and Sentiment Analysis
title_fullStr	Understanding Customers’ Transport Services with Topic Clustering and Sentiment Analysis
title_full_unstemmed	Understanding Customers’ Transport Services with Topic Clustering and Sentiment Analysis
title_sort	understanding customers’ transport services with topic clustering and sentiment analysis
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/af913e071abd487eb257a01c373fe483
work_keys_str_mv	AT alejandromoreno understandingcustomerstransportserviceswithtopicclusteringandsentimentanalysis AT carlosaiglesias understandingcustomerstransportserviceswithtopicclusteringandsentimentanalysis
_version_	1718436405701509120

Understanding Customers’ Transport Services with Topic Clustering and Sentiment Analysis

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