Mining delivery customer claims in social media in Colombia. An exploratory analysis applying machine learning algorithms.

Authors

DOI:

https://doi.org/10.15847/obsOBS18320242382

Abstract

The amount of available data, computational developments, and growing social demands from customers pose enormous challenges for delivery companies. Recognizing this need, we sought to investigate how delivery customer claims through Twitter. For this purpose, we used a sequential mixed methodology, starting with a literature review by applying a bibliometric analysis and its subsequent interpretation through content analysis. Subsequently, we scraped the mentions made by different users to deliver brands on Twitter to consolidate a significant corpus of data that would allow us its subsequent review through an exploratory analysis. Finally, applying natural language processing and machine learning algorithms, we discovered how users of these delivery companies usually compare brands with their competitors when complaining. We specifically highlighted the psychological bias of users, who polarize between love and hate for brands, respond to other users' posts, and achieve meaningful interactions with their likes as the main predictors of this type of claim.

Author Biographies

Manuela Escobar-Sierra, Universidad de Medellín

Manuela Escobar-Sierra, PhD.

School of Economic and Administrative Sciences, University of Medellin, Carrera 87 N° 30 – 65 Building 12 Office 101, Medellín, Colombia. 

e-mail: manuelaescobar@gmail.com

Erica Yaneth Guisao Giraldo, Universidad de Medellín

Erica Yaneth Guisao Giraldo, PhD student in administration.

School of Economic and Administrative Sciences, University of Medellin, Carrera 87 N° 30 – 65 Building 7 Office 105, Medellín, Colombia.

e-mail:  eyguisao@udemedellin.edu.co

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Published

2024-09-28

How to Cite

Escobar-Sierra, M., & Guisao Giraldo, E. Y. (2024). Mining delivery customer claims in social media in Colombia. An exploratory analysis applying machine learning algorithms. Observatorio (OBS*), 18(3). https://doi.org/10.15847/obsOBS18320242382

Issue

Vol. 18 No. 3 (2024)

Section

Articles

License

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