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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
Lexicon-Based Sentiment Analysis
Sentiment Lexicons
Types of Sentiment Lexicons
General-Purpose Lexicons
Domain-Specific Lexicons
Emotion Lexicons
Popular Sentiment Lexicons
SentiWordNet
VADER Lexicon
AFINN Lexicon
TextBlob Lexicon
Bing Liu Lexicon
Lexicon Construction Methods
Manual Construction
Semi-Automatic Construction
Automatic Construction
Lexicon Evaluation
Coverage Analysis
Accuracy Assessment
Domain Adaptation
Dictionary-Based Approaches
Seed Word Selection
Synonym and Antonym Expansion
WordNet-Based Methods
Thesaurus-Based Methods
Corpus-Based Approaches
Statistical Co-occurrence Methods
Point-wise Mutual Information
Bootstrapping Techniques
Graph-Based Propagation
Sentiment Scoring Methods
Simple Aggregation
Weighted Scoring
Compositional Approaches
Context-Aware Scoring
Handling Linguistic Phenomena
Negation Detection and Handling
Negation Cues
Negation Scope
Double Negation
Implicit Negation
Intensification and Diminishment
Intensifier Words
Diminisher Words
Degree Adverbs
Contrastive Expressions
Contrastive Conjunctions
Adversative Relations
Conditional Statements
Rhetorical Questions
Limitations and Challenges
Context Independence
Domain Specificity
Sarcasm and Irony
Ambiguity Issues
Coverage Limitations
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2. Natural Language Processing Foundations
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4. Machine Learning Approaches