This project focuses on performing sentiment analysis on Zomato restaurant reviews. It aims to classify reviews as positive, negative, or neutral based on the text content. The project aims to analyze Zomato restaurant data in India to understand customer sentiments through reviews and visualize the data for insights.
Zomato is a popular online platform that provides information about restaurants, including user reviews. The goal of this project is to develop a machine learning model that can accurately classify the sentiment of Zomato restaurant reviews. The sentiment labels are divided into three categories: positive, negative, and neutral.
The project involves the following key steps:
Data collection: Obtain a dataset of Zomato restaurant reviews, including text content and sentiment labels.
Data preprocessing: Perform necessary preprocessing steps, such as cleaning the text data, removing stopwords, and normalization
Model development: Train and evaluate different machine learning models for sentiment analysis. Consider various algorithms, such as Naive Bayes, Support Vector Machines, Logistic Regression, Random Forest, XGBoost.
Model evaluation: Assess the performance of the developed models using appropriate evaluation metrics, such as accuracy, precision, recall, and F1-score. Compare the results of AUC Score of different models to determine the best-performing one.
Deployment: Once a satisfactory model is obtained, deploy it in a production environment where it can be used to predict sentiment for new, unseen restaurant reviews.
- Inferential Statistics
- Machine Learning
- Data Visualization
- Predictive Modeling
- NLP Data Cleaning
- Python
- Numpy
- Pandas, jupyter