Sentiment Analysis of Customer Reviews of Food Delivery Services Using Deep Learning and Explainable Artificial Intelligence: Systematic Review

被引:29
作者
Adak, Anirban [1 ]
Pradhan, Biswajeet [1 ,2 ,3 ]
Shukla, Nagesh [1 ]
机构
[1] Univ Technol Sydney, Fac Engn & IT, Ctr Adv Modelling & Geospatial Informat Syst CAMG, Sch Civil & Environm Engn, Sydney, NSW 2007, Australia
[2] King Abdulaziz Univ, Ctr Excellence Climate Change Res, POB 80234, Jeddah 21589, Saudi Arabia
[3] Univ Kebangsaan, Earth Observat Ctr, Inst Climate Change, Bangi 43600, Malaysia
关键词
sentiment analysis; food delivery services; deep learning; explainable artificial intelligence; lime; shapley; NEURAL-NETWORKS; SOCIAL MEDIA;
D O I
10.3390/foods11101500
中图分类号
TS2 [食品工业];
学科分类号
0832 ;
摘要
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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页数:16
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