Sentiment Analysis of Restaurant Reviews from Bangladeshi Food Delivery Apps

2023 International Conference on Emerging Smart Computing and Informatics (ESCI) Pub Date : 2023-03-01 DOI:10.1109/ESCI56872.2023.10100214

Ehsanur Rahman Rhythm, Rajvir Ahmed Shuvo, Md Sabbir Hossain, Md. Farhadul Islam, Annajiat Alim Rasel

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Abstract

In this study, we conducted sentiment analysis on restaurant reviews from Bangladeshi food delivery apps using natural language processing techniques. Food delivery apps have become increasingly popular in Bangladesh, and understanding the sentiment of customer reviews can provide valuable insights for restaurant owners and food delivery app companies. In this research, we have created a dataset named “Bangladeshi Restaurant Reviews” by gathering customer reviews of restau-rants available on Foodpanda and Hungrynaki, which are two popular food delivery apps in Bangladesh. We used Robustly Optimized BERT Pretraining Approach (RoBERTa), AFINN, and DistilBERT, a distilled version of Bidirectional Encoder Repre-sentations from Transformers (BERT) to perform the sentiment analysis. Overall, this research paper highlights the importance of sentiment analysis in the food delivery industry and demonstrates the effectiveness of different models in performing this task. It also provides insights for businesses looking to use sentiment analysis to improve their services and products. The accuracy of the models evaluated, RoBERTa, AFINN, and DistilBERT, were 74%, 73 %, and 77 % respectively.

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孟加拉外卖app对餐厅评论的情感分析

在这项研究中，我们使用自然语言处理技术对孟加拉国外卖应用程序的餐厅评论进行了情感分析。外卖应用在孟加拉国越来越受欢迎，了解顾客评论的情绪可以为餐馆老板和外卖应用公司提供有价值的见解。在这项研究中，我们通过收集顾客对Foodpanda和Hungrynaki上餐馆的评论，创建了一个名为“孟加拉国餐馆评论”的数据集。Foodpanda和Hungrynaki是孟加拉国两个受欢迎的外卖应用程序。我们使用鲁棒优化的BERT预训练方法(RoBERTa)、AFINN和蒸馏版的《变形金刚》双向编码器表示(BERT)来执行情感分析。总的来说，本研究论文强调了情感分析在外卖行业的重要性，并展示了不同模型在执行这项任务时的有效性。它还为希望利用情感分析改进服务和产品的企业提供了见解。RoBERTa、AFINN和DistilBERT评估模型的准确率分别为74%、73%和77%。

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2023 International Conference on Emerging Smart Computing and Informatics (ESCI)

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