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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
Natural Language Processing Foundations
Text Preprocessing Pipeline
Text Cleaning
HTML Tag Removal
Special Character Handling
Encoding Issues
Tokenization
Word Tokenization
Sentence Tokenization
Subword Tokenization
Normalization Techniques
Lowercasing
Stop Word Removal
Punctuation Handling
Number Normalization
Morphological Processing
Stemming Algorithms
Lemmatization
Morphological Analysis
Social Media Text Processing
Emoji Handling
Hashtag Processing
URL and Mention Handling
Abbreviation Expansion
Spelling and Grammar Correction
Spell Checking
Grammar Correction
Noise Reduction
Linguistic Analysis
Part-of-Speech Tagging
POS Tag Sets
Tagging Algorithms
Applications in Sentiment Analysis
Syntactic Parsing
Dependency Parsing
Dependency Relations
Dependency Trees
Parser Algorithms
Constituency Parsing
Phrase Structure Grammar
Parse Trees
Grammar Formalisms
Named Entity Recognition
Entity Types
NER Algorithms
Role in Sentiment Analysis
Semantic Analysis
Word Sense Disambiguation
Semantic Role Labeling
Semantic Similarity
Text Representation
Document Formats
Plain Text
Structured Formats
Markup Languages
Character Encoding
Unicode Standards
Encoding Detection
Language Detection
Language Identification Algorithms
Multilingual Text Handling
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1. Introduction to Sentiment Analysis
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3. Lexicon-Based Sentiment Analysis