Optimizing restaurant recommendations through sentiment analysis
This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2024.
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Brac University
2024
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| 在线阅读: | http://hdl.handle.net/10361/23798 |
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10361-237982024-08-19T21:00:50Z Optimizing restaurant recommendations through sentiment analysis Juha, Tasnia Chowdhury, Shaira Trina, Sultana Marium Ami, Md Fardin Rahman Hasme, Abu Obaida Sadeque, Farig Yousuf Department of Computer Science and Engineering, Brac University Restaurant recommendation Sentiment analysis Customer rating Customer review Cuisines BERT LSTM Natural language processing This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2024. Cataloged from PDF version of thesis. Includes bibliographical references (pages 47-49). "The rapid growth of the food industry and dining-out culture has led to a vast growth in the number of restaurants over the years. As a result, customers are overwhelmed with too many options to purchase their meals from. Using sentiment analysis, this study’s goal would therefore be to create a data-driven recommendation system that can provide customers with the best restaurants that serve their preferred cuisine. Unlike previous research, this study includes a model that analyzes reviews in four languages namely English, Bangla, Code Switch (Bangla and English) and Code Mix (Banglish) from a multitude of food and applications. A customer’s choice of restaurant will depend on multiple factors such as the recommendations of friends or food critics, their culinary preference, pricing, location, the general reputation of the restaurant and so on. This study proposes a novel approach that uses sentiment anal- ysis based on primarily sourced restaurant reviews and ratings to provide person- alized restaurant recommendations to interested customers. The assessment of our proposed model will be wide-ranging, through sentiment analysis applying multiple BERT model variants such as BERT for English annotated reviews, BanglaBERT for Bengali reviews, BanglishBERT for Code-Mixed reviews and XLM-RoBERTa for Code-Switched reviews with 79% , 74%, 75% and 77% best model accuracies respectively. Furthermore, this research investigates the analysis of various LSTM architectures, including Attention-LSTM, Bi-LSTM, and a Transformer-based T5 model. Utilizing customer reviews for a variety of restaurants, the efficiency of the model across multiple languages and types of sentiment analysis tasks will be eval- uated throughout the entire set of experiments. The results of this study should provide a time efficient solution for food enthusiasts and the general consumer to be introduced to the finest restaurants with the best gastronomic delights, magnificent ambience and atmosphere and the best customer service. " Tasnia Juha Shaira Chowdhury Sultana Marium Trina Md Fardin Rahman Ami Abu Obaida Hasme B.Sc. in Computer Science 2024-08-19T06:27:37Z 2024-08-19T06:27:37Z 2024 2024-03 Thesis ID 23241038 ID 20101261 ID 20101059 ID 20101549 ID 23341057 http://hdl.handle.net/10361/23798 en Brac University theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. 49 pages application/pdf Brac University |
| institution |
Brac University |
| collection |
Institutional Repository |
| language |
English |
| topic |
Restaurant recommendation Sentiment analysis Customer rating Customer review Cuisines BERT LSTM Natural language processing |
| spellingShingle |
Restaurant recommendation Sentiment analysis Customer rating Customer review Cuisines BERT LSTM Natural language processing Juha, Tasnia Chowdhury, Shaira Trina, Sultana Marium Ami, Md Fardin Rahman Hasme, Abu Obaida Optimizing restaurant recommendations through sentiment analysis |
| description |
This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2024. |
| author2 |
Sadeque, Farig Yousuf |
| author_facet |
Sadeque, Farig Yousuf Juha, Tasnia Chowdhury, Shaira Trina, Sultana Marium Ami, Md Fardin Rahman Hasme, Abu Obaida |
| format |
Thesis |
| author |
Juha, Tasnia Chowdhury, Shaira Trina, Sultana Marium Ami, Md Fardin Rahman Hasme, Abu Obaida |
| author_sort |
Juha, Tasnia |
| title |
Optimizing restaurant recommendations through sentiment analysis |
| title_short |
Optimizing restaurant recommendations through sentiment analysis |
| title_full |
Optimizing restaurant recommendations through sentiment analysis |
| title_fullStr |
Optimizing restaurant recommendations through sentiment analysis |
| title_full_unstemmed |
Optimizing restaurant recommendations through sentiment analysis |
| title_sort |
optimizing restaurant recommendations through sentiment analysis |
| publisher |
Brac University |
| publishDate |
2024 |
| url |
http://hdl.handle.net/10361/23798 |
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