Unboxing Deep Learning Model of Food Delivery Service Reviews Using Explainable Artificial Intelligence (XAI) Technique Article Swipe
Anirban Adak
,
Biswajeet Pradhan
,
Nagesh Shukla
,
Abdullah Alamri
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.3390/foods11142019
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.3390/foods11142019
The demand for food delivery services (FDSs) during the COVID-19 crisis has been fuelled by consumers who prefer to order meals online and have it delivered to their door than to wait at a restaurant. Since many restaurants moved online and joined FDSs such as Uber Eats, Menulog, and Deliveroo, customer reviews on internet platforms have become a valuable source of information about a company’s performance. FDS organisations strive to collect customer complaints and effectively utilise the information to identify improvements needed to enhance customer satisfaction. However, only a few customer opinions are addressed because of the large amount of customer feedback data and lack of customer service consultants. Organisations can use artificial intelligence (AI) instead of relying on customer service experts and find solutions on their own to save money as opposed to reading each review. Based on the literature, deep learning (DL) methods have shown remarkable results in obtaining better accuracy when working with large datasets in other domains, but lack explainability in their model. Rapid research on explainable AI (XAI) to explain predictions made by opaque models looks promising but remains to be explored in the FDS domain. This study conducted a sentiment analysis by comparing simple and hybrid DL techniques (LSTM, Bi-LSTM, Bi-GRU-LSTM-CNN) in the FDS domain and explained the predictions using SHapley Additive exPlanations (SHAP) and Local Interpretable Model-Agnostic Explanations (LIME). The DL models were trained and tested on the customer review dataset extracted from the ProductReview website. Results showed that the LSTM, Bi-LSTM and Bi-GRU-LSTM-CNN models achieved an accuracy of 96.07%, 95.85% and 96.33%, respectively. The model should exhibit fewer false negatives because FDS organisations aim to identify and address each and every customer complaint. The LSTM model was chosen over the other two DL models, Bi-LSTM and Bi-GRU-LSTM-CNN, due to its lower rate of false negatives. XAI techniques, such as SHAP and LIME, revealed the feature contribution of the words used towards positive and negative sentiments, which were used to validate the model.
Related Topics
Concepts
Computer science
Artificial intelligence
Domain (mathematical analysis)
Service (business)
Customer satisfaction
Deep learning
Order (exchange)
The Internet
Customer service
Data science
Machine learning
Operations research
Marketing
Business
World Wide Web
Engineering
Mathematics
Finance
Mathematical analysis
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/foods11142019
- https://www.mdpi.com/2304-8158/11/14/2019/pdf?version=1658136380
- OA Status
- gold
- Cited By
- 38
- References
- 48
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4284957923
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4284957923Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3390/foods11142019Digital Object Identifier
- Title
-
Unboxing Deep Learning Model of Food Delivery Service Reviews Using Explainable Artificial Intelligence (XAI) TechniqueWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
-
2022Year of publication
- Publication date
-
2022-07-08Full publication date if available
- Authors
-
Anirban Adak, Biswajeet Pradhan, Nagesh Shukla, Abdullah AlamriList of authors in order
- Landing page
-
https://doi.org/10.3390/foods11142019Publisher landing page
- PDF URL
-
https://www.mdpi.com/2304-8158/11/14/2019/pdf?version=1658136380Direct link to full text PDF
- Open access
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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/2304-8158/11/14/2019/pdf?version=1658136380Direct OA link when available
- Concepts
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Computer science, Artificial intelligence, Domain (mathematical analysis), Service (business), Customer satisfaction, Deep learning, Order (exchange), The Internet, Customer service, Data science, Machine learning, Operations research, Marketing, Business, World Wide Web, Engineering, Mathematics, Finance, Mathematical analysisTop concepts (fields/topics) attached by OpenAlex
- Cited by
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38Total citation count in OpenAlex
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2025: 12, 2024: 14, 2023: 10, 2022: 2Per-year citation counts (last 5 years)
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48Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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