Citation

Mehedi, Md Humaion Kabir and Farid, Fahmid Al and Rhythm, Ehsanur Rahman and Rahman, Farhin and Hasib, Khan Md and Uddin, Jia and Mansor, Sarina (2025) Deep hierarchical networks for sentiment analysis of restaurant reviews from food apps. Scientific Reports, 15 (1). ISSN 2045-2322

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Abstract

This study presents a framework for conducting sentiment analysis using deep learning techniques to evaluate restaurant food reviews collected from FoodPanda, a food delivery platform. We provide BDFoodSent, a unique large-scale dataset that includes over 334,000 reviews from FoodPanda in Bangladesh. These reviews were annotated for both binary (positive/negative) and multi-class (very bad, bad, neutral, good, and very good) sentiment classifications. Bag of Words (BoW) and TF-IDF feature extraction techniques were used to assess the traditional machine learning (ML) models. The models included decision trees, support vector machines, random forests, XGBoost, and k-nearest neighbors. To solve the issue of class imbalance, we used random oversampling, which resulted in a considerable improvement in the performance of the multi-class classification model. In addition, we present a hybrid deep learning architecture called H-Food. The design included embedding, BiLSTM, attention, and convolutional layers. The results of the experiments show that the proposed model achieved an accuracy of 91.42% and an F1-score of 91.35% when applied to binary classification tasks. Additionally, it achieved an accuracy of 78.74% and an F1-score of 78.72% when applied to multi-class classification using oversampled data. Comparative evaluations showed that the proposed architectures outperformed the classical models and baseline CNN design. The findings of this study demonstrate the efficacy of deep hierarchical networks in comprehending the feelings of users and provide insights that may be applied by restaurant services in emerging countries in the future

Item Type:	Article
Uncontrolled Keywords:	Bangladeshi food, deep learning, food reviews, NLP, restaurant review, sentiment analysis
Subjects:	Q Science > Q Science (General) > Q300-390 Cybernetics
Divisions:	Faculty of Artificial Intelligence & Engineering (FAIE)
Depositing User:	Nor Afiqah Mohd Adnan
Date Deposited:	10 Dec 2025 00:48
Last Modified:	10 Dec 2025 00:48
URII:	http://shdl.mmu.edu.my/id/eprint/14998

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