International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 13 Issue: 02 | Feb 2026
p-ISSN: 2395-0072
www.irjet.net
SENTIMENTAL ANALYSIS ON ZOMATO RESTAURANT REVIEWS USING MACHINE LEARNING Mr. N. Paparayudu1 A. Sahana2, G. Nathaswee3, B. Srikanth4, B. Rohith Kumar5 1Assistant Professor, Department of IT, TKR College of Engineering and Technology, Telangana, India 2,3,4,5B.Tech Students, Department of IT, TKR College of Engineering and Technology, Telangana, India
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Abstract - The rapid growth of online food delivery
way to understand customer opinions by automatically classifying reviews as Positive, Negative, or Neutral. This not only helps restaurants identify strengths and weaknesses in their services but also assists diners in making informed choices based on collective customer experiences. The project “Sentiment Analysis on Zomato Restaurant Reviews using Machine Learning” focuses on leveraging machine learning algorithms to process and analyze customer reviews. The dataset consists of restaurant names, reviewer details, textual reviews, and ratings. The ratings are used to generate sentiment labels, while the textual reviews are preprocessed using techniques such as text normalization, punctuation removal, and tokenization. TF-IDF vectorization is employed to convert the textual data into numerical features, which are then fed into a Logistic Regression model for sentiment classification. Implemented as a Django-based web application, the system provides a user-friendly interface for users to input reviews and obtain real-time sentiment predictions. Administrators can train the model on updated datasets, visualize sentiment distributions through interactive graphs, and manage registered users. By combining machine learning, NLP, and web technologies, this project demonstrates how unstructured review data can be transformed into actionable insights, enhancing customer satisfaction and supporting restaurants in strategic decisionmaking.
platforms has led to an overwhelming volume of customer reviews, making it challenging for restaurants and diners to extract meaningful insights efficiently. This project, “Sentiment Analysis on Zomato Restaurant Reviews using Machine Learning,” aims to develop an intelligent system that automatically classifies customer feedback into Positive, Negative, or Neutral sentiments, thereby enabling data-driven decision-making for both businesses and customers. The system utilizes Natural Language Processing (NLP) techniques to preprocess review texts, including cleaning, normalization, and removal of noise such as punctuation, URLs, and numeric characters. Features are extracted using TF-IDF vectorization, which transforms textual data into numerical representations suitable for machine learning. A Logistic Regression classifier is then trained on labeled review data to predict sentiments accurately. The project is implemented as a Django-based web application with a structured user and admin interface. Users can input new reviews to receive instant sentiment predictions, while administrators can train and update the model with new datasets, visualize sentiment distributions through interactive graphs, and manage registered users. The system not only provides actionable insights for restaurants to enhance service quality and menu offerings but also assists diners in making informed choices based on aggregated feedback. Preliminary results demonstrate that the model achieves high accuracy in sentiment classification, effectively capturing the nuances of customer opinions. This project showcases the integration of machine learning, NLP, and web technologies to transform unstructured review data into meaningful insights, highlighting its potential for scalability and real-world applications in the food service industry.
1.1 Machine Learning-Based Sentiment Analysis on Zomato Restaurant Reviews The system architecture of a machine learning–based sentiment analysis framework for Zomato restaurant reviews. User-generated reviews and ratings collected from the Zomato platform serve as the input data. These reviews are first processed by a review preprocessing module, where text cleaning, tokenization, stop-word removal, and lemmatization are performed to convert raw textual data into a structured format. The processed text is then transformed into numerical features using techniques such as Term Frequency–Inverse Document Frequency (TF-IDF). These features are fed into machine learning and natural language processing models, including Naive Bayes, Support Vector Machine (SVM), and Logistic Regression, to learn sentiment patterns from the data. Finally, the trained models classify the reviews into sentiment categories such as positive, negative, or neutral, along with an overall review
Key Words: Sentiment analysis, Zomato restaurant reviews, machine learning, natural language processing, text classification, opinion mining, customer feedback analysis, supervised learning.
1. INTRODUCTION In today’s digital era, online platforms like Zomato, Yelp, and TripAdvisor have become primary sources of customer feedback for restaurants. With thousands of reviews being posted daily, it becomes difficult for restaurant owners and potential diners to manually analyze and interpret this vast amount of textual data. Sentiment analysis, a subset of Natural Language Processing (NLP), provides a systematic
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