Course Details
This course introduces you to IBM SPSS Modeler’s Text Analytics capabilities, empowering you to analyze unstructured text data and uncover valuable insights. Through practical, hands-on exercises, you’ll learn how to transform text into actionable information for better decision-making.
Key Skills You’ll Gain:
- Text Mining – Extract and structure meaningful information from unstructured text sources
- Sentiment Analysis – Identify and interpret opinions, emotions, and attitudes within text data
- Entity and Concept Extraction – Automatically detect people, places, organizations, and key themes
- Text Categorization – Classify and organize text data for streamlined analysis
By the end of this course, you will be able to use IBM SPSS Modeler to efficiently process text data, create insightful visualizations, and apply analytics techniques that support data-driven strategies in business and research.
What You’ll Learn
This course provides a complete introduction to IBM SPSS Modeler’s Text Analytics capabilities, equipping you with the skills to transform unstructured text into actionable insights. Here are the key skills and techniques you’ll master:
- Fundamentals of Text Analytics – Understand core concepts, methods, and applications across industries
- IBM SPSS Modeler Text Analytics Interface – Navigate and utilize the platform efficiently
- Data Import & Management – Work with unstructured text from multiple sources
- Entity & Concept Extraction – Identify key themes, entities, and topics from text datasets
- Natural Language Processing (NLP) – Clean, parse, and prepare text data for analysis
- Text Mining Projects – Build and refine projects using built-in SPSS Modeler tools
- Text Categorization – Classify text into predefined categories with accuracy
- Sentiment Analysis – Detect positive, negative, or neutral sentiment in text
- Text Visualizations – Create concept maps, word clouds, and relationship diagrams
- Integration with Predictive Modeling – Combine text analytics with other SPSS modeling techniques
- Best Practices – Interpret, report, and present text analytics results effectively
Pro Tip: You’ll gain hands-on experience through real-world text mining scenarios, ensuring you can confidently apply these techniques in professional projects.
Course Content
Requirements
This course is designed to be beginner-friendly and is open to anyone,
regardless of prior experience or background.
Course Description
This course provides a comprehensive introduction to text analytics using IBM
SPSS Modeler, enabling learners to transform unstructured text data into
valuable insights. Participants will explore key concepts of natural language
processing (NLP), including data preparation, entity extraction, sentiment
analysis, and text categorization. Through hands-on exercises, learners will
work with real-world text data, build categorization models, and generate
visualizations such as concept maps and word clouds to uncover hidden
patterns and relationships. The course also covers integration of text analytics
workflows with predictive modeling, allowing participants to combine textual
insights with structured data for deeper analysis. By the end of the course,
learners will be equipped with the skills to interpret, report, and present
findings effectively, supporting data-driven decision-making across industries.
Course Content
Our comprehensive curriculum is organized into 14 modules plus appendix and final assessment, taking you from foundational concepts to advanced text mining techniques in IBM SPSS Modeler:
- Text Mining and Data Mining
- Text Mining Applications
- A Strategy for Data Mining: CRISP-DM
- Stages in CRISP-DM (Business Understanding, Data Understanding, Data Preparation, Modeling, Evaluation, Deployment)
- Exercise: Preparing for a Text-Mining Project
- Test Your Knowledge (5 Questions)
- Review Questions
- Text Mining Nodes
- Text Mining Modeling Node
- Steps in a Typical Text Mining Session
- Demonstration: A Typical Text-Mining Session
- Exercise: Analyzing Customer Opinions
- Test Your Knowledge (3 Questions)
- Review Questions
- File List Node
- File Viewer Node
- Web Feed Node (RSS & HTML)
- Demonstrations: Reading Text from Files and Web Feeds
- Exercise: Mining Data from an RSS Feed
- Test Your Knowledge (4 Questions)
- Review Questions
- Elements of Linguistic Analysis
- Parts of Speech Identification
- Text Preprocessing & Term Extraction
- Equivalence Classes, Forcing, and Excluding
- Concept Categorization & Resource Templates
- Test Your Knowledge (4 Questions)
- Review Questions
- Concept Model Creation
- Model Options & Resource Templates
- Exploring and Scoring Concepts
- Relating Concepts to Other Data
- Exercises and Demonstrations
- Test Your Knowledge (3 Questions)
- Review Questions
- Workbench Views & Panes
- Reviewing & Filtering Extracted Concepts and Types
- Updating Text Mining Nodes
- Exercises and Demonstrations
- Test Your Knowledge (3 Questions)
- Review Questions
- Resource Templates & Libraries
- Dictionary Editing
- Extracting Unextracted Text
- Types vs Synonyms
- Exercise: Editing Dictionaries
- Test Your Knowledge (4 Questions)
- Review Questions
- Fuzzy Grouping & Non-Linguistic Entities
- Language Handling & Extraction Patterns
- Abbreviations & Forced Definitions
- Exercise: Editing Advanced Resources
- Test Your Knowledge (4 Questions)
- Review Questions
- Text Link Analysis Views & Patterns
- Creating & Editing TLA Rules
- Converting Patterns to Categories
- Using the TLA Node
- Exercise: Perform Text Link Analysis
- Test Your Knowledge (4 Questions)
- Review Questions
- Building & Exploring Clusters
- Cluster Analysis Settings
- Cluster Web Graphs & Categories
- Exercise: Cluster Concepts
- Test Your Knowledge (4 Questions)
- Review Questions
- Strategies & Techniques
- Text Analysis Packages
- Importing Predefined Categories
- Automated Classification
- Exercise: Categorization Methods
- Test Your Knowledge (4 Questions)
- Review Questions
- Automated Classification & Rule Creation
- Extending Categories
- Creating Text Analysis Packages
- Exercise: Categorizing Data
- Test Your Knowledge (4 Questions)
- Review Questions
- Template Editor & Libraries
- Publishing & Sharing Resources
- Editing Forced Terms
- Backup & Management
- Exercise: Manage Linguistic Resources
- Test Your Knowledge (4 Questions)
- Review Questions
- Exploring & Developing Models
- Combining Categories with Customer Data
- Scoring New Data
- Exercise: Use Text Mining Models
- Test Your Knowledge (5 Questions)
- Review Questions
- Unit Objectives
- Unit Summary
- Final Exam (15 Questions)
- Completion Certificates
Highlights of this Course:
Upon successful completion of the course, you will receive a Course Completion Certificate and a Project Completion Certificate.
