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https://github.com/md-emon-hasan/ml-project-bangali-comments-sentiment-analysis
💬 Classify Bengali comments as positive or negative. It provides valuable insights for social media and socity to improve satisfaction based on sentiment analysis.
https://github.com/md-emon-hasan/ml-project-bangali-comments-sentiment-analysis
ai bengali bengali-nlp data-science machine-learning nlp sentiment-analysis sentiment-classification text-analysis text-classification
Last synced: about 1 month ago
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💬 Classify Bengali comments as positive or negative. It provides valuable insights for social media and socity to improve satisfaction based on sentiment analysis.
- Host: GitHub
- URL: https://github.com/md-emon-hasan/ml-project-bangali-comments-sentiment-analysis
- Owner: Md-Emon-Hasan
- License: mit
- Created: 2024-11-17T17:14:10.000Z (about 2 months ago)
- Default Branch: master
- Last Pushed: 2024-11-17T18:24:02.000Z (about 2 months ago)
- Last Synced: 2024-11-17T19:25:08.718Z (about 2 months ago)
- Topics: ai, bengali, bengali-nlp, data-science, machine-learning, nlp, sentiment-analysis, sentiment-classification, text-analysis, text-classification
- Language: Jupyter Notebook
- Homepage:
- Size: 15.2 MB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# 📊 **Sentiment Analysis of Bengali Comments**
Welcome to the **Sentiment Analysis of Bengali Comments** repository! This project uses natural language processing (NLP) and machine learning techniques to analyze sentiment from Bengali customer comments. It classifies comments as positive or negative, providing businesses with actionable insights based on customer feedback.
## 📋 Contents
- [Introduction](#introduction)
- [Topics Covered](#topics-covered)
- [Getting Started](#getting-started)
- [Machine Learning Model](#machine-learning-model)
- [Data](#data)
- [Best Practices](#best-practices)
- [FAQ](#faq)
- [Troubleshooting](#troubleshooting)
- [Contributing](#contributing)
- [Additional Resources](#additional-resources)
- [Challenges Faced](#challenges-faced)
- [Lessons Learned](#lessons-learned)
- [Why I Created This Repository](#why-i-created-this-repository)
- [License](#license)
- [Contact](#contact)
---
## 📖 Introduction
This repository presents a **Sentiment Analysis** project on Bengali customer comments. Using NLP and machine learning models, this project aims to classify comments as positive or negative. These insights can assist businesses in improving products and customer satisfaction.
---
## 🔍 Topics Covered
- **NLP Techniques:** Tokenization, text cleaning, and feature extraction using TF-IDF and word embeddings.
- **Sentiment Classification Models:** Using algorithms like Naive Bayes, Logistic Regression, and LSTM networks for sentiment analysis.
- **Data Preprocessing:** Text normalization, stopword removal, and handling missing data.
- **Model Evaluation:** Evaluating models with metrics such as accuracy, precision, recall, F1-score, and ROC-AUC.
---
## 🚀 Getting Started
To get started with this project, follow these steps:
1. **Clone the repository:**
```bash
git clone https://github.com/Md-Emon-Hasan/ML-Project-Bangali-Comments-Sentiment-Analysis.git
```
2. **Navigate to the project directory:**
```bash
cd ML-Project-Bangali-Comments-Sentiment-Analysis
```
3. **Create and activate a virtual environment:**
```bash
python -m venv venv
source venv/bin/activate # For Windows: `venv\Scripts\activate`
```
4. **Install the dependencies:**
```bash
pip install -r requirements.txt
```
5. **Run the analysis script:**
```bash
python analysis.py
```
---
## 🧠 Machine Learning Model
This project utilizes various machine learning models to classify Bengali comments as either positive or negative.
### Key Components:
- **Data Cleaning:** Remove unwanted characters, normalize text, and remove stopwords.
- **Feature Extraction:** Use TF-IDF, word embeddings, or character-level features.
- **Model Training:** Build and train models like Naive Bayes, SVM, and LSTM.
- **Model Evaluation:** Evaluate models using accuracy, precision, recall, and F1-score.
---
## 📊 Data
- The dataset consists of Bengali customer comments, which are preprocessed to extract valuable features for sentiment analysis.
- The features include the text of the comment and its corresponding sentiment label.
- The data is cleaned, tokenized, and transformed into a suitable format for training the models.
---
## 🌟 Best Practices
Suggestions for improving and maintaining this project:
- **Model Optimization:** Experiment with different algorithms and hyperparameters to improve performance.
- **Real-time Analysis:** Explore how to deploy this model as a service for real-time sentiment analysis.
- **Text Preprocessing:** Continue improving text cleaning methods and handle more diverse comment structures.
---
## ❓ FAQ
**Q: What is the goal of this project?**
A: The project aims to classify Bengali comments into positive or negative sentiments, providing insights for businesses.
**Q: How can I contribute to this repository?**
A: Please check the [Contributing](#contributing) section for more details.
**Q: Can I use this model for analyzing new comments?**
A: Yes, the model is designed to analyze new Bengali comments by preprocessing them in the same way as the training data.
---
## 🛠️ Troubleshooting
Common issues and their solutions:
- **Issue: Model performance is low.**
*Solution:* Try improving preprocessing, hyperparameter tuning, or using a different model.
- **Issue: Missing dependencies.**
*Solution:* Ensure the virtual environment is active and install the dependencies using `pip install -r requirements.txt`.
---
## 🤝 Contributing
Contributions are welcome! Here’s how you can contribute:
1. **Fork the repository.**
2. **Create a new branch:**
```bash
git checkout -b feature/new-feature
```
3. **Make your changes.**
4. **Commit the changes:**
```bash
git commit -m 'Add feature or fix issue'
```
5. **Push to the repository and create a pull request.**
---
## 📚 Additional Resources
For further reading on sentiment analysis and NLP:
- **Kaggle Datasets:** [Bangali Comments Dataset](https://www.kaggle.com/yourdatasetlink)
- **NLP Resources:** [NLTK Documentation](https://www.nltk.org/)
- **Deep Learning for NLP:** [DeepLearning.AI NLP Specialization on Coursera](https://www.coursera.org/specializations/natural-language-processing)
---
## 💪 Challenges Faced
Key challenges during development:
- Handling variations in the Bengali language and dialects.
- Improving model accuracy with limited data.
---
## 📚 Lessons Learned
Key takeaways:
- The importance of fine-tuning text preprocessing for sentiment analysis tasks.
- Evaluating models with multiple metrics improves understanding of performance.
---
## 🌟 Why I Created This Repository
This repository was created to explore NLP and machine learning techniques for sentiment analysis, specifically for Bengali comments. The goal is to provide businesses with insights from customer feedback.
---
## 📝 License
This repository is licensed under the [MIT License](https://opensource.org/licenses/MIT). See the [LICENSE](LICENSE) file for more details.
---
## 📬 Contact
- **Email:** [[email protected]](mailto:[email protected])
- **WhatsApp:** [+8801834363533](https://wa.me/8801834363533)
- **GitHub:** [Md-Emon-Hasan](https://github.com/Md-Emon-Hasan)
- **LinkedIn:** [Md Emon Hasan](https://www.linkedin.com/in/md-emon-hasan)
- **Facebook:** [Md Emon Hasan](https://www.facebook.com/mdemon.hasan2001/)
---