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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
Feature Engineering and Representation
Traditional Text Features
Bag-of-Words Representation
Unigrams
Bigrams
Trigrams
Higher-order N-grams
Vocabulary Size Considerations
Term Frequency Methods
Raw Term Frequency
Normalized Term Frequency
Binary Occurrence
TF-IDF Weighting
Term Frequency Component
Inverse Document Frequency
Normalization Schemes
Variants and Extensions
Feature Selection
Chi-Square Test
Mutual Information
Information Gain
Correlation-Based Selection
Recursive Feature Elimination
Linguistic Features
Part-of-Speech Features
POS Tag Frequencies
POS Patterns
Syntactic Features
Dependency Features
Dependency Relations
Syntactic Patterns
Tree Kernels
Semantic Features
Named Entity Features
Semantic Role Features
WordNet Features
Sentiment-Specific Features
Lexicon-Based Features
Sentiment Scores
Emotion Scores
Polarity Counts
Negation Features
Negation Indicators
Negated Terms
Scope Features
Intensification Features
Intensifier Counts
Degree Modifiers
Word Embeddings
Static Word Embeddings
Word2Vec
Continuous Bag-of-Words
Skip-gram Model
Hierarchical Softmax
Negative Sampling
GloVe Embeddings
Global Matrix Factorization
Local Context Windows
FastText
Subword Information
Character N-grams
Out-of-Vocabulary Handling
Contextualized Embeddings
ELMo
Bidirectional Language Models
Context-Dependent Representations
Transformer-Based Models
BERT
RoBERTa
DistilBERT
ALBERT
GPT Models
Embedding Fine-tuning
Domain Adaptation
Task-Specific Fine-tuning
Embedding Alignment
Embedding Evaluation
Intrinsic Evaluation
Extrinsic Evaluation
Visualization Techniques
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4. Machine Learning Approaches
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6. Deep Learning for Sentiment Analysis