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https://github.com/tushard48/amazon-review-sentiment
https://github.com/tushard48/amazon-review-sentiment
Last synced: 11 days ago
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- Host: GitHub
- URL: https://github.com/tushard48/amazon-review-sentiment
- Owner: TusharD48
- Created: 2024-08-01T09:41:31.000Z (5 months ago)
- Default Branch: main
- Last Pushed: 2024-08-01T10:51:21.000Z (5 months ago)
- Last Synced: 2024-11-08T11:43:04.411Z (2 months ago)
- Language: Jupyter Notebook
- Size: 910 KB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# Amazon-Review-Sentiment-Analysis
[](https://ww38.rishavchanda.io/)
# Table of Content
- Introduction
- Project Structure
- Installation
- Usage
- Dataset
- Model
- Results
- Contributing
- License
- Contact
# Introduction
This project aims to perform sentiment analysis on Amazon product reviews. Sentiment analysis is a natural language processing (NLP) technique used to determine whether data is positive, negative, or neutral. By analyzing reviews, businesses can understand customer satisfaction and improve their products and services.
# Project Structure
The project directory is structured as follows:
```bash
amazon-review-sentiment-analysis/
├── data/
│ ├── raw/
│ ├── processed/
├── notebooks/
├── src/
│ ├── data_preprocessing.py
│ ├── model_training.py
│ ├── sentiment_analysis.py
├── models/
├── results/
├── README.md
└── requirements.txt
```
## Installation
```
python -m venv venv
source venv/bin/activate # On Windows, use `venv\Scripts\activate`
pip install pandas numpy scikit-learn nltk
```
# Table of Content
- Introduction
- Project Structure
- Installation
- Usage
- Dataset
- Model
- Results
- Contributing
- License
- Contact
# Introduction
This project aims to perform sentiment analysis on Amazon product reviews. Sentiment analysis is a natural language processing (NLP) technique used to determine whether data is positive, negative, or neutral. By analyzing reviews, businesses can understand customer satisfaction and improve their products and services.
# Model and Technique
This project utilizes several machine learning techniques and models to perform sentiment analysis:
- NLP Model: A model specifically designed for natural language processing tasks. This could be a simple TF-IDF vectorizer combined with a classifier or a more complex deep learning model like BERT.
- Random Forest: An ensemble learning method that operates by constructing multiple decision trees.
- XGBoost: An optimized gradient boosting algorithm designed for speed and performance.
- Grid Search CV: A technique to perform hyperparameter tuning by searching through a manually specified subset of the hyperparameter space.
- K-Fold Cross Validation: A resampling procedure used to evaluate machine learning models on a limited data sample