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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
3.
Lexicon-Based Sentiment Analysis
3.1.
Sentiment Lexicons
3.1.1.
Types of Sentiment Lexicons
3.1.1.1.
General-Purpose Lexicons
3.1.1.2.
Domain-Specific Lexicons
3.1.1.3.
Emotion Lexicons
3.1.2.
Popular Sentiment Lexicons
3.1.2.1.
SentiWordNet
3.1.2.2.
VADER Lexicon
3.1.2.3.
AFINN Lexicon
3.1.2.4.
TextBlob Lexicon
3.1.2.5.
Bing Liu Lexicon
3.1.3.
Lexicon Construction Methods
3.1.3.1.
Manual Construction
3.1.3.2.
Semi-Automatic Construction
3.1.3.3.
Automatic Construction
3.1.4.
Lexicon Evaluation
3.1.4.1.
Coverage Analysis
3.1.4.2.
Accuracy Assessment
3.1.4.3.
Domain Adaptation
3.2.
Dictionary-Based Approaches
3.2.1.
Seed Word Selection
3.2.2.
Synonym and Antonym Expansion
3.2.3.
WordNet-Based Methods
3.2.4.
Thesaurus-Based Methods
3.3.
Corpus-Based Approaches
3.3.1.
Statistical Co-occurrence Methods
3.3.2.
Point-wise Mutual Information
3.3.3.
Bootstrapping Techniques
3.3.4.
Graph-Based Propagation
3.4.
Sentiment Scoring Methods
3.4.1.
Simple Aggregation
3.4.2.
Weighted Scoring
3.4.3.
Compositional Approaches
3.4.4.
Context-Aware Scoring
3.5.
Handling Linguistic Phenomena
3.5.1.
Negation Detection and Handling
3.5.1.1.
Negation Cues
3.5.1.2.
Negation Scope
3.5.1.3.
Double Negation
3.5.1.4.
Implicit Negation
3.5.2.
Intensification and Diminishment
3.5.2.1.
Intensifier Words
3.5.2.2.
Diminisher Words
3.5.2.3.
Degree Adverbs
3.5.3.
Contrastive Expressions
3.5.3.1.
Contrastive Conjunctions
3.5.3.2.
Adversative Relations
3.5.4.
Conditional Statements
3.5.5.
Rhetorical Questions
3.6.
Limitations and Challenges
3.6.1.
Context Independence
3.6.2.
Domain Specificity
3.6.3.
Sarcasm and Irony
3.6.4.
Ambiguity Issues
3.6.5.
Coverage Limitations
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4. Machine Learning Approaches