Digital Humanities in Text Mining and Cultural Data Training Course

Digital Humanities in Text Mining and Cultural Data Training Course

Introduction

In today’s digital age, the intersection of humanities and data science has created new opportunities for analyzing, interpreting, and preserving cultural texts. Digital Humanities in Text Mining and Cultural Data Training Course equips participants with cutting-edge skills to explore large datasets derived from literature, historical archives, media, and other cultural artifacts. Through powerful techniques such as text mining, natural language processing (NLP), and data visualization, learners will discover how to derive meaningful insights and trends from textual sources.

This course is ideal for researchers, librarians, educators, and digital content analysts who want to leverage digital tools to understand human culture in the digital landscape. With hands-on training, real-world case studies, and the use of tools like Voyant Tools, Python (NLTK, spaCy), and Topic Modeling, this program provides both theoretical and practical knowledge to unlock the full potential of digital text analysis.

Course Objectives

1. Understand the foundations of Digital Humanities and computational text analysis.
2. Apply text mining techniques to historical and literary texts.
3. Use NLP tools such as NLTK and spaCy to extract meaning from cultural data.
4. Create interactive data visualizations of textual patterns.
5. Analyze large-scale corpora using automated tools.
6. Employ topic modeling and sentiment analysis in cultural research.
7. Integrate digital storytelling in the humanities using mined data.
8. Explore ethical considerations in digital cultural analysis.
9. Build searchable databases for archival and literary sources.
10. Apply metadata standards for digital cultural collections.
11. Use machine learning for classifying historical documents.
12. Explore multilingual text mining in global humanities projects.
13. Conduct a capstone project on text mining in cultural heritage studies.

Target Audience

1. Digital Humanities Researchers
2. University Faculty & Humanities Instructors
3. Data Analysts in Culture & Media
4. Archivists and Museum Professionals
5. Library and Information Science Scholars
6. Graduate Students in Humanities and Social Sciences
7. Computational Linguists
8. Cultural Heritage Technologists

Course Duration: 5 days

Course Modules

Module 1: Introduction to Digital Humanities

• Definition, scope, and evolution
• Key trends and digital transformation in humanities
• Role of computation in cultural studies
• Overview of tools and platforms
• Challenges in digital archives
• Case Study: Analyzing Shakespeare’s works using Voyant Tools

Module 2: Basics of Text Mining

• What is text mining?
• Data preprocessing and cleaning
• Tokenization, lemmatization, stemming
• Frequency analysis and n-grams
• Keyword extraction and collocations
• Case Study: Mining letters from World War I archives

Module 3: Natural Language Processing (NLP)

• NLP overview in humanities
• Named entity recognition (NER)
• Sentiment analysis
• Part-of-speech tagging
• Word embeddings
• Case Study: Sentiment analysis in 20th-century newspapers

Module 4: Topic Modeling and Text Classification

• Introduction to LDA and other models
• Unsupervised vs supervised learning
• Implementing classifiers (SVM, Naive Bayes)
• Training and evaluating models
• Interpreting model results
• Case Study: Classifying themes in African postcolonial literature

Module 5: Data Visualization in Humanities

• Principles of visualizing text data
• Tools: Tableau, Gephi, D3.js
• Word clouds, timelines, network graphs
• Geographic mapping of texts
• Ethical visualization practices
• Case Study: Mapping migration stories through digital storytelling

Module 6: Digital Archives and Metadata

• Understanding metadata standards (Dublin Core, TEI)
• Digital preservation best practices
• Creating searchable digital collections
• OCR and digitization workflows
• Open data and interoperability
• Case Study: Building a digital library for indigenous narratives

Module 7: Multilingual and Global Text Mining

• Text mining across languages
• Machine translation for analysis
• Corpus creation in non-English texts
• Bias in multilingual models
• Cross-cultural NLP challenges
• Case Study: Mining colonial-era French and Swahili texts

Module 8: Capstone Project and Research Design

• Designing a digital humanities research project
• Data collection and ethical approvals
• Tool selection and integration
• Documentation and reproducibility
• Presentation and publishing
• Case Study: Student-led cultural analytics on diaspora literature

Training Methodology

• Instructor-led virtual/onsite sessions
• Practical hands-on labs using real cultural datasets
• Weekly assignments and reflection logs
• Peer-reviewed capstone projects
• Continuous feedback and mentoring
• Access to digital repositories and research tools

Register as a group from 3 participants for a Discount

Send us an email: [email protected] or call +254724527104 

Certification

Upon successful completion of this training, participants will be issued with a globally- recognized certificate.

Tailor-Made Course

 We also offer tailor-made courses based on your needs.

Key Notes

a. The participant must be conversant with English.

b. Upon completion of training the participant will be issued with an Authorized Training Certificate

c. Course duration is flexible and the contents can be modified to fit any number of days.

d. The course fee includes facilitation training materials, 2 coffee breaks, buffet lunch and A Certificate upon successful completion of Training.

e. One-year post-training support Consultation and Coaching provided after the course.

f. Payment should be done at least a week before commence of the training, to DATASTAT CONSULTANCY LTD account, as indicated in the invoice so as to enable us prepare better for you.