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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
Advanced Topics and Challenges
Sarcasm and Irony Detection
Sarcasm Characteristics
Detection Approaches
Lexical Features
Contextual Features
Pragmatic Features
Irony Detection
Datasets and Benchmarks
Evaluation Challenges
Emotion Analysis
Emotion Models
Categorical Models
Ekman's Basic Emotions
Plutchik's Wheel
Dimensional Models
Valence-Arousal Model
PAD Model
Emotion Detection Methods
Lexicon-Based Approaches
Machine Learning Approaches
Deep Learning Approaches
Multi-Label Emotion Classification
Emotion Intensity Prediction
Negation Handling
Negation Cue Detection
Scope Determination
Negation Resolution
Complex Negation Patterns
Evaluation Methods
Multilingual Sentiment Analysis
Language-Specific Challenges
Cross-Lingual Methods
Machine Translation
Cross-Lingual Embeddings
Multilingual Models
Code-Switching
Low-Resource Languages
Multimodal Sentiment Analysis
Text-Image Fusion
Text-Audio Fusion
Text-Video Fusion
Feature Extraction Methods
Fusion Strategies
Multimodal Datasets
Temporal Sentiment Analysis
Time-Aware Models
Sentiment Evolution
Trend Analysis
Dynamic Sentiment
Real-Time Processing
Streaming Data Handling
Online Learning
Scalability Issues
Latency Optimization
Distributed Processing
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7. Aspect-Based Sentiment Analysis
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9. Evaluation and Metrics