Analyzing Customer Reviews on Food Delivery Services Using Deep Learning and Explainable Artificial Intelligence (XAI)

Adak, Anirban

Analyzing Customer Reviews on Food Delivery Services Using Deep Learning and Explainable Artificial Intelligence (XAI)

Adak, Anirban

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Publication Type:: Thesis
Issue Date:: 2022

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Field	Value	Language
dc.contributor.author	Adak, Anirban
dc.date.accessioned	2023-03-27T02:59:48Z
dc.date.available	2023-03-27T02:59:48Z
dc.date.issued	2022
dc.identifier.uri	http://hdl.handle.net/10453/168563
dc.description	University of Technology Sydney. Faculty of Engineering and Information Technology.	en_US.UTF-8
dc.description.abstract	Social media reviews and feedback are getting increasingly important for customers ordering food from a food delivery services in the last few years. This trend has become even more prominent since COVID-19 pandemic and government enforced lockdowns. During the Covid-19 crisis, customer’s preferences in having food delivered to their doorstep instead of waiting in a restaurant has propelled the growth of food delivery services (FDS). As all restaurants go online and get onboarded to FDS, such as UberEATS, Menulog or Deliveroo, customer review on online platforms has become an important source of information about the company’s performance. The FDS organisations would like to find complaints from customer feedback and use the data effectively to understand the areas for improvement to enhance customer satisfaction. The study aims to review the Machine Learning (ML) and Deep Learning (DL) models along with explainable artificial intelligence (XAI) method to predict customer sentiment in the FDS domain. This research aims to develop a robust end-to-end framework using AI/ML which can help to accurately predict customer sentiment. Also, it presents the XAI technique implementation on the black box DL models to verify the results. Finally, positive and negative sentiments are grouped using topic categorization technique.	en_US.UTF-8
dc.format	Thesis (Msc(CompSc))
dc.language.iso	en_US	en_US.UTF-8
dc.relation	https://opus.lib.uts.edu.au/bitstream/10453/168563/2/02whole.pdf
dc.rights	info:eu-repo/semantics/openAccess
dc.rights	The author owns the copyright in this thesis including all reproduction and reuse rights for the work. The work may not be altered without the permission of the copyright owner. Attribution is essential when quoting or paraphrasing from this thesis.
dc.rights	© 2022 Anirban Adak
dc.rights	au.edu.uts.lib/ppc
dc.title	Analyzing Customer Reviews on Food Delivery Services Using Deep Learning and Explainable Artificial Intelligence (XAI)	en_US.UTF-8
dc.type	Thesis
utslib.copyright.status	open_access	*

Abstract:

Social media reviews and feedback are getting increasingly important for customers ordering food from a food delivery services in the last few years. This trend has become even more prominent since COVID-19 pandemic and government enforced lockdowns. During the Covid-19 crisis, customer’s preferences in having food delivered to their doorstep instead of waiting in a restaurant has propelled the growth of food delivery services (FDS). As all restaurants go online and get onboarded to FDS, such as UberEATS, Menulog or Deliveroo, customer review on online platforms has become an important source of information about the company’s performance. The FDS organisations would like to find complaints from customer feedback and use the data effectively to understand the areas for improvement to enhance customer satisfaction. The study aims to review the Machine Learning (ML) and Deep Learning (DL) models along with explainable artificial intelligence (XAI) method to predict customer sentiment in the FDS domain. This research aims to develop a robust end-to-end framework using AI/ML which can help to accurately predict customer sentiment. Also, it presents the XAI technique implementation on the black box DL models to verify the results. Finally, positive and negative sentiments are grouped using topic categorization technique.

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http://hdl.handle.net/10453/168563