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https://github.com/bniladridas/sentiment-analysis
A sentiment analysis project using Naive Bayes in Python.
https://github.com/bniladridas/sentiment-analysis
jupyter-notebook pandas python scikit-learn
Last synced: about 2 months ago
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A sentiment analysis project using Naive Bayes in Python.
- Host: GitHub
- URL: https://github.com/bniladridas/sentiment-analysis
- Owner: bniladridas
- License: mit
- Created: 2024-05-25T07:49:02.000Z (8 months ago)
- Default Branch: main
- Last Pushed: 2024-09-23T10:09:43.000Z (4 months ago)
- Last Synced: 2024-10-20T05:40:52.501Z (3 months ago)
- Topics: jupyter-notebook, pandas, python, scikit-learn
- Language: Jupyter Notebook
- Homepage:
- Size: 15.6 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
Awesome Lists containing this project
README
# 🏆 **Sentiment Analysis with Naive Bayes Classifier** 🏆
Welcome to the **Sentiment Analysis** project, where we harness the power of a Naive Bayes Classifier to classify text into sentiments. Whether you're a data enthusiast or an ML explorer, this repository is a perfect guide to understanding sentiment analysis through a simple, yet powerful machine learning model.
## 🌟 **Project Overview**
Dive deep into sentiment analysis by training a Naive Bayes classifier on labeled text data to categorize sentiments into **positive**, **negative**, or **neutral**. This project walks through the data preprocessing, model training, and evaluation process. Perfect for those eager to learn how Naive Bayes, one of the most efficient algorithms for text classification, works in action.
---
## 🚀 **Technologies Employed**
- **Language**: Python 🐍
- **Libraries**:
- **scikit-learn** for building and evaluating the Naive Bayes model
- **pandas** for data manipulation and preprocessing
---
## 🛠 **Tooling**
- **Jupyter Notebook** for an interactive and visual workflow.
- **Git** for version control.
---
## ⚙️ **Installation Guide**
To get started with this project locally, follow these simple steps:
1. **Clone the Repository:**
```bash
git clone https://github.com/niladrridas/sentiment-analysis-naive-bayes.git
```
2. **Navigate to the Project Directory:**
```bash
cd sentiment-analysis-naive-bayes
```
3. **Install the Required Dependencies:**
```bash
pip install -r requirements.txt
```
---
## 📘 **How to Use the Project**
1. Fire up Jupyter Notebook:
```bash
jupyter notebook main_py.ipynb
```
2. Run through the notebook step-by-step:
- **Data Loading & Exploration**: Import and inspect the dataset.
- **Data Preprocessing**: Clean and prepare text data for model training.
- **Model Training**: Train the Naive Bayes classifier on the dataset.
- **Evaluation**: Evaluate model performance using accuracy, precision, and recall.
---
## 📊 **Results and Insights**
By leveraging the power of Naive Bayes, you’ll be able to classify text data with a high degree of accuracy, perfect for real-world applications like product reviews, customer feedback, and social media sentiment monitoring.
**Looking to scale it up?** The project lays the foundation to experiment with more advanced models such as SVM, LSTM, or Transformers. Boost your sentiment analysis game with larger datasets and more sophisticated techniques!
---
## 🏅 **Conclusion**
This project highlights how even a simple Naive Bayes Classifier can be effectively used for **Sentiment Analysis**. It showcases end-to-end workflow from data preprocessing to model evaluation, providing a solid stepping stone into the vast world of machine learning for NLP.
Want to push the boundaries? Try integrating deep learning models or experimenting with new NLP techniques to improve accuracy! The possibilities are endless.
---
🌟 Happy Coding! 🎉