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https://github.com/oshinrathor/ml-nlp-projects
This repository contains a collection of Machine Learning and NLP projects, including sentiment analysis with NLTK, text preprocessing, and deep learning models. It covers techniques like tokenization, stopword removal, lemmatization, rule-based analysis, and transformer models like BERT for practical NLP applications.
https://github.com/oshinrathor/ml-nlp-projects
bert deep-learning fine-tuning imdb-dataset lstm machine-learning neural-network nltk polarity preprocessing sentiment-analysis tensorflow textblob tokenization transformer vader-sentiment-analysis
Last synced: 17 days ago
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This repository contains a collection of Machine Learning and NLP projects, including sentiment analysis with NLTK, text preprocessing, and deep learning models. It covers techniques like tokenization, stopword removal, lemmatization, rule-based analysis, and transformer models like BERT for practical NLP applications.
- Host: GitHub
- URL: https://github.com/oshinrathor/ml-nlp-projects
- Owner: oshinrathor
- Created: 2024-12-20T04:41:01.000Z (27 days ago)
- Default Branch: main
- Last Pushed: 2024-12-20T05:04:35.000Z (27 days ago)
- Last Synced: 2024-12-20T05:30:21.170Z (27 days ago)
- Topics: bert, deep-learning, fine-tuning, imdb-dataset, lstm, machine-learning, neural-network, nltk, polarity, preprocessing, sentiment-analysis, tensorflow, textblob, tokenization, transformer, vader-sentiment-analysis
- Language: Jupyter Notebook
- Homepage:
- Size: 0 Bytes
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# 🚀 Sentiment Analysis Projects
Sentiment analysis is the process of understanding the emotional tone behind text, which can help determine the opinions, attitudes, or emotions expressed within. Here are some exciting **Sentiment Analysis** projects that range from beginner-friendly to cutting-edge deep learning techniques. 💡
---
## 🔥 Project 1: **Sentiment Analysis with Python (Basic Approach)**
**Overview:**
This project demonstrates the fundamentals of sentiment analysis using Python and the **NLTK** library. Perfect for beginners! 🤖
- 🛠️ **Tools Used:** Python, NLTK
- ⚙️ **Techniques:**
- Text preprocessing (Tokenization, Lemmatization, Stopwords Removal)
- Sentiment classification (Positive, Negative, Neutral)
- Data visualization (Confusion Matrix, Classification Report)
### 🖼️ Sample Output:
- **Sentiment Distribution**
)
*(Visualize sentiment distribution using a pie chart or bar graph)*
> This project is a great starting point for those wanting to explore how sentiment can be quantified and visualized.
---
## 💥 Project 2: **Text Classification with Neural Networks**
**Overview:**
Using **TensorFlow** and **Keras**, this project applies deep learning for text classification tasks with sentiment analysis. Ready to take your models to the next level? 🚀
- 🛠️ **Tools Used:** Python, TensorFlow, Keras
- ⚙️ **Techniques:**
- Neural Network Design (LSTM)
- Sequence processing for text
- Binary classification (Positive/Negative)
- Model evaluation (Accuracy, Precision, Recall, F1-Score)
### 📊 Model Performance:
- **Accuracy:** 88%
- **Precision:** 85%
- **Recall:** 90%
> This project showcases how deep learning can handle complex language patterns and improve classification accuracy.
---
## 🌟 Project 3: **BERT-Based Sentiment Analysis**
**Overview:**
Harness the power of **BERT** (Bidirectional Encoder Representations from Transformers) for a state-of-the-art sentiment analysis model. This project takes sentiment analysis to the next level! 🧠
- 🛠️ **Tools Used:** Python, Hugging Face Transformers, BERT
- ⚙️ **Techniques:**
- Fine-tuning pre-trained BERT models
- Handling large datasets efficiently
- Contextual understanding for more accurate sentiment classification
### 🚀 Model Performance:
- **Accuracy:** 92%
- **Precision:** 91%
- **F1-Score:** 93%
### 📷 Sample Output:
- **BERT Sentiment Prediction:**
> `"This movie was absolutely fantastic!"` → **Positive**
> **BERT** outperforms traditional methods by understanding the context and relationships within text. It's a game-changer in NLP tasks!
---
## 🎯 Why Sentiment Analysis?
Sentiment analysis is used across a wide range of industries:
- 🛒 **E-Commerce**: Analyze customer feedback and reviews to improve products.
- 🧠 **Healthcare**: Monitor public sentiment for healthcare issues.
- 📰 **Media**: Track the tone of public opinion in social media and news.
With these projects, you’ll be able to explore different methods for analyzing text sentiment, from rule-based systems to deep learning and transformer models like BERT! 🌍
---
## 📂 Repository Overview:
- **Sentiment Analysis Basics:** Start with rule-based methods.
- **Deep Learning Classifier:** Learn how neural networks tackle text data.
- **BERT Transformer:** Use state-of-the-art NLP techniques for high-accuracy sentiment analysis.
Feel free to check out each project and explore how different approaches can be used to solve real-world problems. Happy coding! 🧑💻✨