Text Data Foundations

Unstructured text data require specialized techniques for cleaning, tokenizing, analyzing frequencies, generating word clouds, and extracting sentiment. This section also covers advanced use cases like fetching Twitter data in real-time for brand monitoring or event analysis.

Unstructured data differs from structured formats by lacking predefined schemas. This section explains what text data is, why it matters, and how text mining begins. Learners gain insights into working with vast textual sources (emails, social media, logs) and the steps needed to create a corpus for analysis.

OVERVIEW OF UNSTRUCTURED DATA

Learning Objectives

• Differentiate text-based unstructured data from structured data

• Recognize the challenges and value of unstructured sources (emails, social media, logs)

Indicative Content

• Examples

  • Emails, chat messages, web pages, sensor text logs

• Growth & Importance

  • Social media scale, real-time data usage

INTRODUCTION TO TEXT MINING

Learning Objectives

• Define text mining (corpus creation, cleaning, transformation)

• Distinguish between data retrieval (search) vs. discovery (finding hidden patterns)

Indicative Content

• Content Analysis

  • Themes, entities, sentiments

• Workflow Steps

  • Data ingestion → cleaning → tokenizing → analyzing frequencies

TOOLS & METHODOLOGIES (TEXT DATA FOUNDATIONS)

• Python Libraries

  • Basic text handling: nltk or similar for data loading/cleaning

• Data Flow

  • Import unstructured text → check format → store in corpus

• Exploration

  • Initial text overview, identification of relevant fields, potential data retrieval vs. deeper analytics