Introduction

In today’s data-driven world, unstructured data comprises over 80% of all data generated, creating a growing demand for professionals skilled in Advanced Text Mining and Information Extraction (IE) techniques. Advanced Text Mining and Information Extraction Training Course is designed to equip participants with cutting-edge skills in natural language processing (NLP), semantic analysis, machine learning, and automated knowledge discovery, ensuring the ability to extract actionable insights from massive textual datasets. Whether dealing with academic literature, social media content, or enterprise documents, this course enables you to master the tools and techniques necessary for accurate, scalable, and intelligent text analytics.

Participants will explore trending technologies like deep learning for NLP, entity recognition, relationship extraction, sentiment analysis, and topic modeling. With real-world case studies across sectors such as finance, healthcare, and governance, learners will gain hands-on experience in deploying advanced text mining models using Python, R, and open-source NLP frameworks. The training integrates theory and practical skills for transforming raw text into structured, meaningful knowledge, aligning with current demands in data science and AI-driven analytics.

Programme Curriculum

Advanced Text Mining and Information Extraction Training Course

Introduction

Course Objectives

Understand the fundamentals of text mining and natural language processing.
Apply machine learning techniques for intelligent text classification.
Perform named entity recognition (NER) and relationship extraction.
Utilize sentiment analysis to derive opinion-based insights.
Conduct topic modeling using LDA and other algorithms.
Implement deep learning models for advanced NLP tasks.
Leverage Python and R libraries for scalable text processing.
Extract structured information from unstructured sources.
Analyze social media and real-time text streams.
Evaluate text mining model performance and accuracy.
Deploy information extraction pipelines in real-world settings.
Interpret results for business intelligence and decision-making.
Build domain-specific applications using text analytics.

Target Audiences

Data Scientists
AI/Machine Learning Engineers
Business Intelligence Analysts
Academic Researchers
Policy Analysts
Software Developers
Journalists & Media Analysts
Public Sector & NGO Researchers

Course Duration: 5 days

Course Modules

Module 1: Introduction to Text Mining and NLP

Overview of unstructured data challenges
Core concepts of NLP and linguistic preprocessing
Text normalization, tokenization, stemming, and lemmatization
Introduction to key Python and R libraries
Exploratory text analysis and frequency-based techniques
Case Study: Analyzing customer feedback from e-commerce platforms

Module 2: Text Classification and Clustering

Supervised vs. unsupervised learning
Algorithms for text classification (Naïve Bayes, SVM)
Document clustering using k-means and hierarchical clustering
Feature engineering and vectorization (TF-IDF, word embeddings)
Evaluating classifier performance (precision, recall, F1)
Case Study: Spam email detection using classification models

Module 3: Named Entity Recognition and Relationship Extraction

NER fundamentals and linguistic patterns
Rule-based vs. statistical models
Extracting entities, attributes, and relations
Dependency parsing and semantic role labeling
Tools: spaCy, Stanford NLP, OpenNLP
Case Study: Extracting financial entities from SEC filings

Module 4: Sentiment Analysis and Opinion Mining

Lexicon-based and machine learning-based approaches
Sentiment scoring and polarity detection
Handling sarcasm, negation, and domain-specific vocabularies
Emotion detection from texts
Visualizing sentiment trends
Case Study: Sentiment analysis of tweets during election campaigns

Module 5: Topic Modeling and Semantic Analysis

Introduction to Latent Dirichlet Allocation (LDA)
Non-negative Matrix Factorization (NMF)
Word embeddings: Word2Vec, GloVe
Visualization of topics using pyLDAvis
Interpreting and labeling latent topics
Case Study: Topic extraction from academic research papers

Module 6: Deep Learning for NLP

Introduction to deep learning concepts
Word embeddings and sequence modeling
RNNs, LSTMs, and Transformers (BERT, GPT)
Fine-tuning pre-trained models for domain tasks
Frameworks: TensorFlow, Hugging Face Transformers
Case Study: Automating summarization of legal documents

Module 7: Social Media Mining and Real-Time Analysis

Text mining on Twitter, Facebook, Reddit
APIs for data collection and preprocessing
Trend detection and event extraction
Real-time sentiment tracking dashboards
Ethics in social media mining
Case Study: Real-time crisis monitoring using Twitter feeds

Module 8: Information Extraction Pipelines and Applications

Building scalable IE workflows
Integration with business intelligence systems
Use of knowledge graphs and ontologies
Deployment and monitoring of text mining systems
Translating insights into business value
Case Study: Implementing an IE pipeline for healthcare policy analysis

Training Methodology

Instructor-led lectures and real-time demonstrations
Practical hands-on coding sessions using Python and R
Group-based project work and collaborative tasks
Peer review and feedback sessions on case studies
Assessments and quizzes to evaluate comprehension
Certification upon successful course completion

Register as a group from 3 participants for a Discount

Send us an email: info@fineskilltrainingcenter.org or call +254769199797

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 Fineskill Training Center account, as indicated in the invoice so as to enable us prepare better for you.

Skills & Software Covered

Text mining NLP information extraction

Programme Fee

Duration: 5 Days

Method: Online or Physical

Online Price: $1,000

Physical Price: $1,500

Certification: Accredited Certificate

View Available Dates 📄 Download Course Outline (PDF)

Available Sessions

Location

Month

Year

Oct 19 2026

19 Oct — 23 Oct 2026

💻 Online / Virtual session

Online $1,000