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https://github.com/deepramazumder/hotel-reviews-sentiment-analysis
A Machine Learning project to predict sentiments from hotel reviews for automated guest satisfaction analysis
https://github.com/deepramazumder/hotel-reviews-sentiment-analysis
lightgbm logistic-regression machine-learning naive-bayes nlp random-forest streamlit web-scraping xgboost
Last synced: 5 days ago
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A Machine Learning project to predict sentiments from hotel reviews for automated guest satisfaction analysis
- Host: GitHub
- URL: https://github.com/deepramazumder/hotel-reviews-sentiment-analysis
- Owner: DeepraMazumder
- License: mit
- Created: 2024-08-31T16:27:50.000Z (2 months ago)
- Default Branch: main
- Last Pushed: 2024-09-07T10:29:22.000Z (2 months ago)
- Last Synced: 2024-09-07T11:41:47.122Z (2 months ago)
- Topics: lightgbm, logistic-regression, machine-learning, naive-bayes, nlp, random-forest, streamlit, web-scraping, xgboost
- Language: Jupyter Notebook
- Homepage: https://innsi8hts-ai.streamlit.app/
- Size: 68.1 MB
- Stars: 0
- Watchers: 1
- Forks: 2
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Hotel Reviews Sentiment Analysis
Welcome to my **Hotel Sentiment Analysis** project! This repository contains all the necessary components to scrape, analyze, predict and summarise sentiments from hotel reviews.
## 🚀 Project Overview
Our project focuses on predicting **positive & negative sentiments** from hotel reviews using a combination of advanced Natural Language Processing (NLP) techniques and classical Machine Learning models. We aim to provide a robust solution that can assist hotels in understanding guest satisfaction through automated sentiment analysis.
## 📂 Project Structure
- **Artifacts**:
- `NPN_Logistic_Regression_Model.pkl`: Logistic Regression model for comparison.
- `NPN_Random_Forest_Model.pkl`: Random Forest model for advanced predictions.
- `NPN_Naive_Bayes_Model.pkl`: Naive Bayes model used for baseline performance.
- `NPN_XGBoost_Model.pkl`: XGBoost model for high-performance predictions.
- `NPN_LightGBM_Model.pkl`: LightGBM model trained for sentiment analysis.
- `NPN_Label_Encoder.pkl`: Pre-trained label encoder for categorical variables.
- `NPN_TF_IDF_Vectorizer.pkl`: TF-IDF vectorizer to transform text data.
- **Dataset**:
- `Scraped_Dataset.csv`: The dataset scraped from various hotel review sites.
- `Single_Hotel_Dataset.csv`: Dataset focusing on a single hotel's reviews.
- **notebooks**:
- `Hotel_Sentiment_Analysis.ipynb`: The Jupyter notebook detailing the model training and evaluation.
- **src**:
- `__init__.py`: Initialization for the source module.
- `prediction.py`: Contains functions for making sentiment predictions.
- `summariser.py`: Script for summarizing reviews and key sentiments.
- `utils.py`: Utility functions used throughout the project.
- **templates**:
- `img/`: Images and media files used in the project.
- **Web_Scraping**:
- `scraper.py`: The web scraping script to extract reviews from online sources.
- `test.py`: Testing scripts to validate the scraper's performance.
- `.gitignore`: Files and folders to be ignored by Git.
- `requirements.txt`: Python packages required to run the project.
- `.streamlit/`: Streamlit configuration files for deploying the web app.
- `streamlit_app.py`: The main Streamlit application file that launches the web interface for the project, allowing users to interact with the sentiment analysis model and visualize the results.
- `setup.py`: Setup script for easy installation of the project.
## 🛠️ Getting Started
### Prerequisites
Make sure you have Python installed. Clone this repository and install the required packages:
```bash
git clone https://github.com/your-repo/NPN-Cognizant-Hackathon.git
cd NPN-Cognizant-Hackathon
pip install -r requirements.txt
```
### Running the Project
1. **Scrape Data**: Use the web scraper to collect hotel reviews.
```bash
python Web_Scraping/test.py
```
2. **Run Analysis**: Execute the Jupyter notebook to train models and analyze sentiments.
```bash
jupyter notebook notebooks/Hotel_Sentiment_Analysis.ipynb
```
3. **Deploy the App**: Deploy the Streamlit web app to showcase your results.
```bash
streamlit run streamlit_app.py
```
## 🧠 Model Overview
- **Logistic Regression**: Baseline model for comparison.
- **Random Forest**: Ensemble method to capture complex patterns.
- **Naive Bayes**: Quick and interpretable model.
- **LightGBM & XGBoost**: Gradient boosting models for high accuracy.
## 📈 Results
Our models have been fine-tuned and evaluated to achieve high accuracy in predicting sentiment from hotel reviews. Detailed results can be found in the notebook.
## 📝 License
This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.