Textual Analysis

Textual analysis, also known as text mining, is a discipline at the intersection of Computer Science and Data Science that involves using computational and statistical techniques to extract meaningful information and patterns from unstructured text data. Leveraging methods from Natural Language Processing (NLP), practitioners can perform tasks such as sentiment analysis to gauge opinion, topic modeling to identify key themes, and named entity recognition to pull out specific people or places. The ultimate goal is to transform qualitative text into quantitative, structured data, enabling analysts to uncover insights, understand trends, and make data-driven decisions from vast collections of documents, social media posts, customer reviews, and other text-based sources.

1. Foundations of Textual Analysis
   1. Defining Textual Analysis
      1. Core Definition and Scope
      2. Distinction between Textual Analysis and Text Mining
      3. Distinction between Textual Analysis and Content Analysis
      4. Historical Development of Textual Analysis
      5. Evolution from Manual to Computational Methods
   2. Relationship to Natural Language Processing
      1. Overlap with NLP Tasks
      2. Differences from Traditional Linguistics
      3. Computational Linguistics Foundations
      4. Statistical vs Rule-Based Approaches
   3. Relationship to Data Science and Computer Science
      1. Integration with Data Science Workflows
      2. Role in Artificial Intelligence
      3. Machine Learning Applications
      4. Information Retrieval Connections
   4. Core Concepts and Terminology
      1. Corpus
         1. Definition and Purpose
         2. Types of Corpora
            1. Monolingual Corpora
            2. Multilingual Corpora
            3. Parallel Corpora
            4. Comparable Corpora
         3. Corpus Construction and Curation
         4. Corpus Size and Representativeness
         5. Balanced vs Specialized Corpora
      2. Document
         1. Document Definition and Boundaries
         2. Document Structure
            1. Paragraphs
            2. Sections
            3. Metadata
            4. Headers and Footers
         3. Document Types
            1. Articles
            2. Emails
            3. Social Media Posts
            4. Academic Papers
            5. Legal Documents
      3. Token
         1. Definition and Granularity
            1. Word-Level Tokens
            2. Subword Tokens
            3. Character-Level Tokens
         2. Tokenization Challenges
            1. Compound Words
            2. Punctuation Handling
            3. Contractions
            4. Hyphenated Words
      4. Vocabulary
         1. Vocabulary Size and Coverage
         2. Out-of-Vocabulary Words
         3. Vocabulary Growth and Zipf's Law
         4. Active vs Passive Vocabulary
      5. Text Structure
         1. Unstructured vs Structured Data
         2. Semi-Structured Text
         3. Characteristics of Unstructured Text
         4. Converting Unstructured to Structured Data
   5. Common Applications and Use Cases
      1. Business Intelligence
         1. Customer Feedback Analysis
         2. Market Trend Analysis
         3. Competitive Intelligence
         4. Product Review Analysis
      2. Social Media Monitoring
         1. Brand Sentiment Tracking
         2. Misinformation Detection
         3. Trend Analysis
         4. Influencer Identification
      3. Academic Research
         1. Literary Analysis
         2. Social Science Research
         3. Historical Text Analysis
         4. Linguistic Research
      4. Healthcare Analytics
         1. Clinical Text Mining
         2. Electronic Health Record Analysis
         3. Medical Literature Review
         4. Drug Adverse Event Detection
      5. Legal Document Review
         1. Contract Analysis
         2. E-Discovery
         3. Legal Precedent Research
         4. Compliance Monitoring
      6. Government and Public Policy
         1. Policy Document Analysis
         2. Public Opinion Mining
         3. Legislative Text Analysis