@article{understanding-gsi-article-2021,
author = "Moreno Garc{\'i}a, Alejandro and Iglesias, Carlos A.",
abstract = "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",
comments = "JCR Q2 2.838, Citescore 3.7",
issn = "2076-3417",
journal = "Applied Sciences",
keywords = "twitter;natural language processing;Customer service;transportation systems",
month = "October",
number = "10169",
pages = "1-18",
title = "{U}nderstanding {C}ustomers’ {T}ransport {S}ervices with {T}opic {C}lustering and {S}entiment {A}nalys",
url = "https://www.mdpi.com/2076-3417/11/21/10169/htm",
volume = "11",
year = "2021",
}