Sentiment Analysis

2.

Natural Language Processing Foundations

2.1.

Text Preprocessing Pipeline

2.1.1.

2.1.1.1.

HTML Tag Removal

2.1.1.2.

Special Character Handling

2.1.1.3.

Encoding Issues

2.1.2.

2.1.2.1.

Word Tokenization

2.1.2.2.

Sentence Tokenization

2.1.2.3.

Subword Tokenization

2.1.3.

Normalization Techniques

2.1.3.1.

2.1.3.2.

Stop Word Removal

2.1.3.3.

Punctuation Handling

2.1.3.4.

Number Normalization

2.1.4.

Morphological Processing

2.1.4.1.

Stemming Algorithms

2.1.4.2.

2.1.4.3.

Morphological Analysis

2.1.5.

Social Media Text Processing

2.1.5.1.

2.1.5.2.

Hashtag Processing

2.1.5.3.

URL and Mention Handling

2.1.5.4.

Abbreviation Expansion

2.1.6.

Spelling and Grammar Correction

2.1.6.1.

2.1.6.2.

Grammar Correction

2.1.6.3.

Noise Reduction

2.2.

Linguistic Analysis

2.2.1.

Part-of-Speech Tagging

2.2.1.1.

2.2.1.2.

Tagging Algorithms

2.2.1.3.

Applications in Sentiment Analysis

2.2.2.

Syntactic Parsing

2.2.2.1.

Dependency Parsing

2.2.2.1.1.

Dependency Relations

2.2.2.1.2.

Dependency Trees

2.2.2.1.3.

Parser Algorithms

2.2.2.2.

Constituency Parsing

2.2.2.2.1.

Phrase Structure Grammar

2.2.2.2.2.

2.2.2.2.3.

Grammar Formalisms

2.2.3.

Named Entity Recognition

2.2.3.1.

2.2.3.2.

2.2.3.3.

Role in Sentiment Analysis

2.2.4.

Semantic Analysis

2.2.4.1.

Word Sense Disambiguation

2.2.4.2.

Semantic Role Labeling

2.2.4.3.

Semantic Similarity

2.3.

Text Representation

2.3.1.

Document Formats

2.3.1.1.

2.3.1.2.

Structured Formats

2.3.1.3.

Markup Languages

2.3.2.

Character Encoding

2.3.2.1.

Unicode Standards

2.3.2.2.

Encoding Detection

2.3.3.

Language Detection

2.3.3.1.

Language Identification Algorithms

2.3.3.2.

Multilingual Text Handling

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1. Introduction to Sentiment Analysis

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3. Lexicon-Based Sentiment Analysis