The week's pick
Random Articles
Reseach Article
Beyond Likes and Loves: Uncovering Complex Sentiments in Bengali Food Reviews on Facebook
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 187 - Number 35 |
| Year of Publication: 2025 |
| Authors: Ataf Fazledin Ahamed, Sadia Sharmin |
10.5120/ijca2025925106
|
Ataf Fazledin Ahamed, Sadia Sharmin . Beyond Likes and Loves: Uncovering Complex Sentiments in Bengali Food Reviews on Facebook. International Journal of Computer Applications. 187, 35 ( Aug 2025), 1-8. DOI=10.5120/ijca2025925106
Abstract
With the rapid growth of internet access, Bangladeshi social media users have been observed to become increasingly active, with Facebook having been established as the primary platform for communication and content sharing. Among various content types, food reviews have been recognized as gaining significant popularity, particularly through video-based posts created by influencers. In this study, the sentiment expressed in Bengali food reviews on Facebook has been investigated through a dual approach: interactionbased sentiment analysis has been performed using Facebook reactions, and sequence classification has been carried out using BanglaBERT, a state-of-the-art Bengali language model. Data have been collected from 905 food review videos and 26,004 associated comments that have been posted between 2020 and 2022. The BanglaBERT model has been fine-tuned on multiple Bengali sentiment datasets, and a prediction accuracy of 83.76% has been achieved, demonstrating its ability to capture nuanced patterns in user opinions. It has been found that the majority of viewers have expressed positive sentiment towards food-related content, although a small subset of comments has reflected contrasting negative opinions. These insights have highlighted the strong engagement value of food reviews in Bangladeshi social media, while the presence of subtle critical perspectives has also been emphasized. A deeper understanding of user perception in this domain has been contributed by the study, and practical implications for content creators, businesses, and researchers in natural language processing have been offered.
References
- Sadia Alam, Md Farhan Ishmam, Navid Hasin Alvee, Md Shahnewaz Siddique, Md Azam Hossain, and Abu Raihan Mostofa Kamal. Bnsentmix: A diverse bengali-english code-mixed dataset for sentiment analysis. arXiv preprint arXiv:2408.08964, 2024.
- Abhik Bhattacharjee, Tahmid Hasan, Wasi Uddin Ahmad, Kazi Samin, Md Saiful Islam, Anindya Iqbal, M Sohel Rahman, and Rifat Shahriyar. Banglabert: Language model pretraining and benchmarks for low-resource language understanding evaluation in bangla. arXiv preprint arXiv:2101.00204, 2021.
- Fatema Tuj Johora Faria, Mukaffi Bin Moin, Rabeya Islam Mumu, Md Mahabubul Alam Abir, Abrar Nawar Alfy, and Mohammad Shafiul Alam. Motamot: A dataset for revealing the supremacy of large language models over transformer models in bengali political sentiment analysis. arXiv preprint arXiv:2407.19528, 2024.
- Cole Freeman, Mrinal Kanti Roy, Michele Fattoruso, and Hamed Alhoori. Shared feelings: Understanding facebook reactions to scholarly articles. In 2019 ACM/IEEE Joint Conference on Digital Libraries (JCDL), pages 301–304. IEEE, 2019.
- Shilpa Gite, Abhishek Udanshiv, Kartik Jaisinghani, Abhishek Singh, Prafful Chetwani, et al. Sentiment analysis techniques on food reviews using machine learning. In Proceedings of Second International Conference on Computing, Communications, and Cyber-Security, pages 511–528. Springer, 2021.
- Alvi Md Ishmam and Sadia Sharmin. Hateful speech detection in public facebook pages for the bengali language. In 2019 18th IEEE international conference on machine learning and applications (ICMLA), pages 555–560. IEEE, 2019.
- Khondoker Ittehadul Islam and Md. Alam. Bangdsa: A largescale dataset and benchmark for document-level sentiment analysis in bangla. Findings of the Association for Computational Linguistics: EMNLP, pages 3265–3271, 2024.
- Khondoker Ittehadul Islam, Sudipta Kar, Md Saiful Islam, and Mohammad Ruhul Amin. Sentnob: A dataset for analysing sentiment on noisy bangla texts. In Findings of the Association for Computational Linguistics: EMNLP 2021, pages 3265–3271, 2021.
- Md. Islam et al. Bangla-senti: A large-scale annotated corpus of 536k youtube comments for sentiment analysis. arXiv preprint arXiv:2501.01234.
- Nourin Islam, Nasrin Akter, and Abdus Sattar. Sentiment analysis on food review using machine learning approach. In 2021 International Conference on Artificial Intelligence and Smart Systems (ICAIS), pages 157–164. IEEE, 2021.
- S. Kemp. Digital 2023: Bangladesh — datareportal – global digital insights, 2023. [Online]. Available: https://datareportal.com/reports/ digital-2023-bangladesh. [Accessed: 20-May-2023].
- Md Serajus Salekin Khan, Sanjida Reza Rafa, Amit Kumar Das, et al. Sentiment analysis on bengali facebook comments to predict fan’s emotions towards a celebrity. Journal of Engineering Advancements, 2(03):118–124, 2021.
- Akos Lada, Meihong Wang, and Tak Yan. How does news feed predict what you want to see?, 01 2021. Meta Newsroom.
- Hemal Mahmud and Hasan Mahmud. Enhancing sentiment analysis in bengali texts: A hybrid approach using lexiconbased algorithm and pretrained language model bangla-bert. arXiv preprint arXiv:2411.19584, 2025.
- Ahmad R Pratama. Sentiment analysis of facebook posts through special reactions: the case of learning from home in indonesia amid covid-19. Jurnal Ilmiah Teknik Elektro Komputer Dan Informatika, 8(1):83–92, 2022.
- Md Atikur Rahman and Emon Kumar Dey. Datasets for aspect-based sentiment analysis in bangla and its baseline evaluation. Data, 3(2):15, 2018.
- Ehsanur Rahman Rhythm and Md Humaion Kabir Mehedi. Bdfoodreview: A large-scale sentiment-labeled restaurant review dataset from bangladesh. Dataset on Mendeley, 2024. Version 2, 334,119 reviews, mixed Bangla/English/Banglish.
- Omar Sharif, Mohammed Moshiul Hoque, and Eftekhar Hossain. Sentiment analysis of bengali texts on online restaurant reviews using multinomial na¨ıve bayes. In 2019 1st international conference on advances in science, engineering and robotics technology (ICASERT), pages 1–6. IEEE, 2019.
Index Terms
Keywords