Description

Introduction of Text Processing in KNIME Analytics

The L4-TP: Text Processing in KNIME Analytics Platform training is a comprehensive program designed for data analysts, data scientists, and NLP practitioners who want to extract, process, and analyze unstructured text data efficiently. This course covers key concepts such as text preprocessing, feature engineering, sentiment analysis, named entity recognition (NER), topic modeling, text clustering, and deep learning for NLP using KNIME’s built-in tools and extensions. Participants will gain hands-on experience in working with real-world datasets to build scalable text analytics pipelines.

Prerequisites 

• Basic knowledge of KNIME Analytics Platform
• Familiarity with data preprocessing and transformation
• Understanding of Natural Language Processing (NLP) concepts (recommended but not required)
• Basic knowledge of machine learning and text mining techniques

Table of Contents

1. Introduction to Text Processing in KNIME

• 1.1 Overview of NLP and Text Mining in KNIME
• 1.2 Importance of Text Analytics in Business and Research
• 1.3 Introduction to KNIME Text Processing Extension
• 1.4 Understanding Unstructured vs. Structured Text Data
• 1.5 Setting Up the KNIME Environment for Text Analytics

2. Text Data Collection and Preprocessing

• 2.1 Importing Text Data from Different Sources (CSV, PDFs, Web Scraping, APIs)
• 2.2 Handling and Parsing Different Text Formats (JSON, XML, HTML)
• 2.3 Tokenization, Stopword Removal, and Lemmatization
• 2.4 Handling Case Sensitivity, Punctuation, and Special Characters
• 2.5 Text Normalization and Noise Reduction Techniques
• 2.6 Stemming and Lemmatization – When to Use Which?

3. Feature Engineering and Text Vectorization

• 3.1 Creating the Term-Document Matrix (TDM)
• 3.2 TF-IDF and Word Frequency Analysis
• 3.3 Bag-of-Words (BoW) vs. Word Embeddings
• 3.4 Generating N-grams for Improved Text Representations
• 3.5 POS (Part-of-Speech) Tagging for Text Understanding
• 3.6 Feature Selection Techniques for Text Data

4. Sentiment Analysis and Opinion Mining

• 4.1 Understanding Sentiment Analysis Techniques
• 4.2 Rule-Based vs. Machine Learning-Based Sentiment Classification
• 4.3 Implementing Sentiment Scoring using KNIME Sentiment Analysis Nodes
• 4.4 Using Pre-trained Sentiment Models and Custom Dictionaries
• 4.5 Visualizing Sentiment Trends with KNIME and Power BI

5. Topic Modeling and Text Clustering

• 5.1 Understanding Latent Dirichlet Allocation (LDA) for Topic Modeling
• 5.2 Implementing LDA and Non-Negative Matrix Factorization (NMF)
• 5.3 Clustering Documents using K-Means, Hierarchical Clustering, and DBSCAN
• 5.4 Evaluating Topic Modeling and Clustering Results
• 5.5 Using Topic Modeling for News, Social Media, and Legal Documents

6. Named Entity Recognition (NER) and Information Extraction

• 6.1 What is Named Entity Recognition (NER)?
• 6.2 Extracting Names, Locations, Organizations, and Custom Entities
• 6.3 Implementing NER using Pre-Trained Models in KNIME(Ref: L4-DE: Data Engineering in KNIME Analytics Platform)
• 6.4 Building Custom NER Models with Machine Learning
• 6.5 Integrating Knowledge Graphs for Advanced NER Applications

7. Text Classification Using Machine Learning

• 7.1 Supervised vs. Unsupervised Text Classification
• 7.2 Training and Evaluating Machine Learning Models for NLP
• 7.3 Using Decision Trees, Naïve Bayes, SVM, and Random Forests
• 7.4 Deep Learning Approaches to Text Classification
• 7.5 Deploying and Automating Text Classification Pipelines

8. Advanced NLP with Deep Learning in KNIME

• 8.1 Introduction to Deep Learning for Text Processing
• 8.2 Implementing Word Embeddings (Word2Vec, GloVe, FastText)
• 8.3 Text Classification with Recurrent Neural Networks (RNNs)
• 8.4 Sentiment Analysis using Long Short-Term Memory (LSTM) Networks
• 8.5 Named Entity Recognition (NER) with Transformer Models (BERT)
• 8.6 Transfer Learning for NLP Tasks in KNIME

9. Text Summarization and Keyword Extraction

• 9.1 Extractive vs. Abstractive Text Summarization
• 9.2 Implementing TextRank for Keyword and Summary Extraction
• 9.3 Using Pre-Trained Models for Automatic Summarization
• 9.4 Practical Applications in News and Legal Document Summarization

10. Text Analytics Visualization and Reporting

• 10.1 Word Clouds, Heatmaps, and Network Analysis for Text Data
• 10.2 Interactive Dashboards for Text Insights (KNIME + Tableau/Power BI)
• 10.3 Automated Reporting for Text Analytics Workflows

11. Real-World Use Cases and Applications

• 11.1 Customer Feedback and Social Media Analysis
• 11.2 Fake News Detection and Media Monitoring
• 11.3 Text Mining in Healthcare (Medical Reports, Clinical Notes)
• 11.4 Legal Document Analysis and Contract Review
• 11.5 E-Commerce and Product Review Analysis

12. Deployment, Automation, and Workflow Orchestration

• 12.1 Deploying Text Analytics Pipelines on KNIME Server
• 12.2 Scheduling and Automating Workflows for NLP Tasks
• 12.3 Best Practices for Scalable Text Processing Pipelines

13. Certification Preparation and Final Assessment

• 13.1 Key Topics for Certification
• 13.2 Practice Exercises and Mock Tests
• 13.3 Capstone Project and Final Review

Conclusion

The L4-TP: Text Processing in KNIME Analytics Platform training provides a comprehensive, hands-on approach to mastering text analytics and NLP in KNIME. By covering text preprocessing, feature engineering, sentiment analysis, NER, topic modeling, and deep learning, this course equips learners with industry-relevant skills to tackle real-world text data challenges. Participants will also gain experience in automating workflows and deploying scalable solutions, preparing them for KNIME certification and advanced NLP applications.

Reference