Evaluating Uber Customers' Perception Through Machine Learning Techniques: A Case Study in Ecuador

Maria Becerra-Salas; Henry N. Roa

doi:10.1109/CSCI62032.2023.00131

Evaluating Uber Customers' Perception Through Machine Learning Techniques: A Case Study in Ecuador

Maria Becerra-Salas, Henry N. Roa

Producción científica: Capítulo del libro/informe/acta de congreso › Contribución de conferencia › revisión exhaustiva

Resumen

This study analyzes the perception of Uber users through Twitter, currently known as X, using the CRISP-DM methodology in Python. We collected data from the last twelve years to accomplish this study. The data set is divided into training and testing, processing them using natural language processing and classifying them as neutral, positive, and hostile. Classification algorithms such as Logistic Regression, Support Vector Machines (SVM), and Naive Bayes are applied, with SVM being the most effective in predicting user sentiments. This approach leverages Twitter accessibility and data analytics to understand the public perception of Uber.

Idioma original	Inglés
Título de la publicación alojada	Proceedings - 2023 International Conference on Computational Science and Computational Intelligence, CSCI 2023
Editorial	Institute of Electrical and Electronics Engineers Inc.
Páginas	771-776
Número de páginas	6
ISBN (versión digital)	9798350361513
DOI	https://doi.org/10.1109/CSCI62032.2023.00131
Estado	Publicada - 2023
Evento	2023 International Conference on Computational Science and Computational Intelligence, CSCI 2023 - Las Vegas, Estados Unidos Duración: 13 dic. 2023 → 15 dic. 2023

Serie de la publicación

Nombre	Proceedings - 2023 International Conference on Computational Science and Computational Intelligence, CSCI 2023

Conferencia

Conferencia	2023 International Conference on Computational Science and Computational Intelligence, CSCI 2023
País/Territorio	Estados Unidos
Ciudad	Las Vegas
Período	13/12/23 → 15/12/23

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Publisher Copyright:
© 2023 IEEE.

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Este resultado contribuye a los siguientes Objetivos de Desarrollo Sostenible

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10.1109/CSCI62032.2023.00131

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Becerra-Salas, M., & Roa, H. N. (2023). Evaluating Uber Customers' Perception Through Machine Learning Techniques: A Case Study in Ecuador. En Proceedings - 2023 International Conference on Computational Science and Computational Intelligence, CSCI 2023 (pp. 771-776). (Proceedings - 2023 International Conference on Computational Science and Computational Intelligence, CSCI 2023). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CSCI62032.2023.00131

Becerra-Salas, Maria ; Roa, Henry N. / Evaluating Uber Customers' Perception Through Machine Learning Techniques : A Case Study in Ecuador. Proceedings - 2023 International Conference on Computational Science and Computational Intelligence, CSCI 2023. Institute of Electrical and Electronics Engineers Inc., 2023. pp. 771-776 (Proceedings - 2023 International Conference on Computational Science and Computational Intelligence, CSCI 2023).

@inproceedings{c92ff3cd9a6c4210b32dc9d7282da341,

title = "Evaluating Uber Customers' Perception Through Machine Learning Techniques: A Case Study in Ecuador",

abstract = "This study analyzes the perception of Uber users through Twitter, currently known as X, using the CRISP-DM methodology in Python. We collected data from the last twelve years to accomplish this study. The data set is divided into training and testing, processing them using natural language processing and classifying them as neutral, positive, and hostile. Classification algorithms such as Logistic Regression, Support Vector Machines (SVM), and Naive Bayes are applied, with SVM being the most effective in predicting user sentiments. This approach leverages Twitter accessibility and data analytics to understand the public perception of Uber.",

keywords = "Artificial intelligence, CRISP-DM, CSCI-RTNA, Full/Regular Research Paper, Machine learning, Sentiment analysis, Twitter (X)",

author = "Maria Becerra-Salas and Roa, {Henry N.}",

note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 2023 International Conference on Computational Science and Computational Intelligence, CSCI 2023 ; Conference date: 13-12-2023 Through 15-12-2023",

year = "2023",

doi = "10.1109/CSCI62032.2023.00131",

language = "English",

series = "Proceedings - 2023 International Conference on Computational Science and Computational Intelligence, CSCI 2023",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "771--776",

booktitle = "Proceedings - 2023 International Conference on Computational Science and Computational Intelligence, CSCI 2023",

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}

Becerra-Salas, M & Roa, HN 2023, Evaluating Uber Customers' Perception Through Machine Learning Techniques: A Case Study in Ecuador. En Proceedings - 2023 International Conference on Computational Science and Computational Intelligence, CSCI 2023. Proceedings - 2023 International Conference on Computational Science and Computational Intelligence, CSCI 2023, Institute of Electrical and Electronics Engineers Inc., pp. 771-776, 2023 International Conference on Computational Science and Computational Intelligence, CSCI 2023, Las Vegas, Estados Unidos, 13/12/23. https://doi.org/10.1109/CSCI62032.2023.00131

Evaluating Uber Customers' Perception Through Machine Learning Techniques: A Case Study in Ecuador. / Becerra-Salas, Maria; Roa, Henry N.
Proceedings - 2023 International Conference on Computational Science and Computational Intelligence, CSCI 2023. Institute of Electrical and Electronics Engineers Inc., 2023. p. 771-776 (Proceedings - 2023 International Conference on Computational Science and Computational Intelligence, CSCI 2023).

Producción científica: Capítulo del libro/informe/acta de congreso › Contribución de conferencia › revisión exhaustiva

TY - GEN

T1 - Evaluating Uber Customers' Perception Through Machine Learning Techniques

T2 - 2023 International Conference on Computational Science and Computational Intelligence, CSCI 2023

AU - Becerra-Salas, Maria

AU - Roa, Henry N.

PY - 2023

Y1 - 2023

N2 - This study analyzes the perception of Uber users through Twitter, currently known as X, using the CRISP-DM methodology in Python. We collected data from the last twelve years to accomplish this study. The data set is divided into training and testing, processing them using natural language processing and classifying them as neutral, positive, and hostile. Classification algorithms such as Logistic Regression, Support Vector Machines (SVM), and Naive Bayes are applied, with SVM being the most effective in predicting user sentiments. This approach leverages Twitter accessibility and data analytics to understand the public perception of Uber.

AB - This study analyzes the perception of Uber users through Twitter, currently known as X, using the CRISP-DM methodology in Python. We collected data from the last twelve years to accomplish this study. The data set is divided into training and testing, processing them using natural language processing and classifying them as neutral, positive, and hostile. Classification algorithms such as Logistic Regression, Support Vector Machines (SVM), and Naive Bayes are applied, with SVM being the most effective in predicting user sentiments. This approach leverages Twitter accessibility and data analytics to understand the public perception of Uber.

KW - Artificial intelligence

KW - CRISP-DM

KW - CSCI-RTNA

KW - Full/Regular Research Paper

KW - Machine learning

KW - Sentiment analysis

KW - Twitter (X)

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DO - 10.1109/CSCI62032.2023.00131

M3 - Conference contribution

AN - SCOPUS:85200009185

T3 - Proceedings - 2023 International Conference on Computational Science and Computational Intelligence, CSCI 2023

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BT - Proceedings - 2023 International Conference on Computational Science and Computational Intelligence, CSCI 2023

PB - Institute of Electrical and Electronics Engineers Inc.

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Becerra-Salas M, Roa HN. Evaluating Uber Customers' Perception Through Machine Learning Techniques: A Case Study in Ecuador. En Proceedings - 2023 International Conference on Computational Science and Computational Intelligence, CSCI 2023. Institute of Electrical and Electronics Engineers Inc. 2023. p. 771-776. (Proceedings - 2023 International Conference on Computational Science and Computational Intelligence, CSCI 2023). doi: 10.1109/CSCI62032.2023.00131

Evaluating Uber Customers' Perception Through Machine Learning Techniques: A Case Study in Ecuador

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