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Computer Science
Data Science
Sentiment Analysis
1. Introduction to Sentiment Analysis
2. Natural Language Processing Foundations
3. Lexicon-Based Sentiment Analysis
4. Machine Learning Approaches
5. Feature Engineering and Representation
6. Deep Learning for Sentiment Analysis
7. Aspect-Based Sentiment Analysis
8. Advanced Topics and Challenges
9. Evaluation and Metrics
10. Practical Implementation and Deployment
11. Ethical Considerations and Bias
12. Current Research and Future Directions
Deep Learning for Sentiment Analysis
Neural Network Fundamentals
Perceptrons and Multi-layer Perceptrons
Activation Functions
Loss Functions
Optimization Algorithms
Regularization Techniques
Recurrent Neural Networks
Vanilla RNNs
Architecture
Vanishing Gradient Problem
Long Short-Term Memory
LSTM Architecture
Forget Gate
Input Gate
Output Gate
Bidirectional LSTM
Stacked LSTM
Gated Recurrent Units
GRU Architecture
Reset Gate
Update Gate
Comparison with LSTM
Convolutional Neural Networks for Text
1D Convolutions
Multiple Filter Sizes
Pooling Operations
Multi-channel CNNs
CNN-LSTM Combinations
Attention Mechanisms
Attention Concept
Self-Attention
Multi-Head Attention
Attention Visualization
Transformer Architecture
Encoder-Decoder Structure
Position Encoding
Layer Normalization
Feed-Forward Networks
Pre-trained Language Models
Transfer Learning Principles
Fine-tuning Strategies
Domain Adaptation
Few-Shot Learning
Advanced Architectures
Hierarchical Attention Networks
Memory Networks
Graph Neural Networks
Capsule Networks
Training Considerations
Data Augmentation
Batch Processing
Learning Rate Scheduling
Early Stopping
Model Checkpointing
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5. Feature Engineering and Representation
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7. Aspect-Based Sentiment Analysis