Sentiment Analysis of Customer Reviews of Food Delivery Services Using Deep Learning and Explainable Artificial Intelligence: Systematic Review Article Swipe
Anirban Adak
,
Biswajeet Pradhan
,
Nagesh Shukla
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.3390/foods11101500
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.3390/foods11101500
During the COVID-19 crisis, customers’ preference in having food delivered to their doorstep instead of waiting in a restaurant has propelled the growth of food delivery services (FDSs). With all restaurants going online and bringing FDSs onboard, such as UberEATS, Menulog or Deliveroo, customer reviews on online platforms have become an important source of information about the company’s performance. FDS organisations aim to gather complaints from customer feedback and effectively use the data to determine the areas for improvement to enhance customer satisfaction. This work aimed to review machine learning (ML) and deep learning (DL) models and explainable artificial intelligence (XAI) methods to predict customer sentiments in the FDS domain. A literature review revealed the wide usage of lexicon-based and ML techniques for predicting sentiments through customer reviews in FDS. However, limited studies applying DL techniques were found due to the lack of the model interpretability and explainability of the decisions made. The key findings of this systematic review are as follows: 77% of the models are non-interpretable in nature, and organisations can argue for the explainability and trust in the system. DL models in other domains perform well in terms of accuracy but lack explainability, which can be achieved with XAI implementation. Future research should focus on implementing DL models for sentiment analysis in the FDS domain and incorporating XAI techniques to bring out the explainability of the models.
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Metadata
- Type
- review
- Language
- en
- Landing Page
- https://doi.org/10.3390/foods11101500
- https://www.mdpi.com/2304-8158/11/10/1500/pdf?version=1653118507
- OA Status
- gold
- Cited By
- 117
- References
- 63
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4281255712
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4281255712Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3390/foods11101500Digital Object Identifier
- Title
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Sentiment Analysis of Customer Reviews of Food Delivery Services Using Deep Learning and Explainable Artificial Intelligence: Systematic ReviewWork title
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reviewOpenAlex work type
- Language
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enPrimary language
- Publication year
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2022Year of publication
- Publication date
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2022-05-21Full publication date if available
- Authors
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Anirban Adak, Biswajeet Pradhan, Nagesh ShuklaList of authors in order
- Landing page
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https://doi.org/10.3390/foods11101500Publisher landing page
- PDF URL
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https://www.mdpi.com/2304-8158/11/10/1500/pdf?version=1653118507Direct link to full text PDF
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YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
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https://www.mdpi.com/2304-8158/11/10/1500/pdf?version=1653118507Direct OA link when available
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Interpretability, Computer science, Sentiment analysis, Artificial intelligence, Domain (mathematical analysis), Deep learning, Preference, Data science, Mathematical analysis, Mathematics, Economics, MicroeconomicsTop concepts (fields/topics) attached by OpenAlex
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117Total citation count in OpenAlex
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2025: 47, 2024: 35, 2023: 29, 2022: 6Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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