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:

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.

   python Web_Scraping/test.py

2. Run Analysis: Execute the Jupyter notebook to train models and analyze sentiments.

   jupyter notebook notebooks/Hotel_Sentiment_Analysis.ipynb

3. Deploy the App: Deploy the Streamlit web app to showcase your results.

   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 file for details.