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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
Aspect-Based Sentiment Analysis
ABSA Task Definition
Aspect Term Extraction
Aspect Category Detection
Aspect Sentiment Classification
Opinion Target Identification
Opinion Holder Extraction
Aspect Extraction Techniques
Rule-Based Methods
Pattern Matching
Linguistic Rules
Domain Knowledge
Frequency-Based Methods
Term Frequency Analysis
Mutual Information
Association Rules
Supervised Learning Approaches
Sequence Labeling
Conditional Random Fields
Hidden Markov Models
Neural Sequence Models
Unsupervised Methods
Topic Modeling
Clustering
Association Mining
Deep Learning Approaches
LSTM-based Extraction
CNN-based Extraction
Attention-based Models
BERT for Aspect Extraction
Aspect Sentiment Classification
Feature-Based Approaches
Aspect-Specific Features
Context Features
Syntactic Features
Neural Network Approaches
Aspect-Aware Neural Networks
Attention-Based Models
Memory Networks
Joint Models
Joint Aspect and Sentiment
Multi-Task Learning
End-to-End Models
Implicit Aspect Handling
Implicit Aspect Detection
Aspect Inference
Context-Based Methods
Multi-Aspect Sentiment
Aspect Interaction
Aspect Hierarchy
Aspect Aggregation
Evaluation of ABSA Systems
Aspect Extraction Metrics
Sentiment Classification Metrics
End-to-End Evaluation
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6. Deep Learning for Sentiment Analysis
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8. Advanced Topics and Challenges