Introduction

In today’s world, vast amounts of unstructured text data are generated every day across multiple domains, from social media posts to customer reviews and legal documents. Text Mining and Analytics allows organizations to extract valuable insights from this data, enabling improved decision-making, trend detection, and automation. This 5-day comprehensive training will dive deep into natural language processing (NLP), machine learning models for text analytics, and advanced techniques like topic modeling, sentiment analysis, and text classification.

By the end of this course, participants will gain the practical skills necessary to handle large-scale text data and build impactful NLP applications.

Objectives

By the end of this course, participants will:

1. Understand the core concepts of text mining and NLP.
2. Preprocess and clean text data for analysis.
3. Implement advanced text mining techniques, such as topic modeling, clustering, and dimensionality reduction.
4. Use machine learning and deep learning models for text classification and sentiment analysis.
5. Apply advanced NLP models (transformers like BERT, GPT, etc.) for complex text analytics.
6. Deploy text mining solutions in real-world use cases (e.g., customer feedback analysis, chatbots).
7. Work with cloud-based NLP platforms for scalable solutions.

Who Should Attend?

• Data Scientists & Data Analysts
• Machine Learning Engineers & NLP Enthusiasts
• Business Intelligence & Marketing Professionals
• Researchers in Social Sciences, Healthcare, and Finance
• Anyone interested in building advanced text analytics applications

Course Outline (5 Days)

Day 1: Introduction to Text Mining & Preprocessing

Morning Session

• Introduction to Text Mining

  • Overview of Text Mining, Natural Language Processing (NLP), and Text Analytics
  • Text mining vs. traditional data analysis
  • Text-based data sources: Social Media, Web Scraping, Customer Reviews, Documents
  • Hands-on: Setting up a Python environment for text mining

• Text Preprocessing Techniques

  • Tokenization, stemming, and lemmatization
  • Removing stop words, punctuation, and noise
  • Text normalization and feature extraction: TF-IDF, Bag of Words
  • Hands-on: Preprocessing raw text data for analysis

Afternoon Session

• Text Representation and Vectorization

  • Word embeddings: Word2Vec, GloVe, FastText
  • Introduction to document embeddings
  • Hands-on: Creating vectorized representations of text using TF-IDF and Word2Vec

• Hands-on Exercise

  • Preprocessing and vectorizing customer reviews data for sentiment analysis

Day 2: Text Classification & Sentiment Analysis

Morning Session

• Supervised Learning for Text Classification

  • Overview of text classification algorithms: Logistic Regression, SVM, Naive Bayes
  • Feature selection and engineering for text data
  • Hands-on: Building a text classifier using Naive Bayes for spam detection

• Sentiment Analysis

  • Sentiment classification models and techniques
  • Evaluating sentiment using positive, negative, and neutral labels
  • Hands-on: Sentiment analysis of Twitter data using NLP models

Afternoon Session

• Deep Learning for Text Classification

  • Using Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) for text data
  • Hands-on: Training an LSTM model for sentiment classification on a movie review dataset

• Hands-on Exercise

  • Building a multi-class sentiment analysis model using deep learning

Day 3: Topic Modeling & Text Clustering

Morning Session

• Introduction to Topic Modeling

  • Understanding Latent Dirichlet Allocation (LDA)
  • Extracting meaningful topics from large text corpora
  • Hands-on: Performing topic modeling using LDA on news articles

• Text Clustering

  • Overview of clustering algorithms: K-Means, DBSCAN, Agglomerative Clustering
  • Text clustering vs. text classification
  • Hands-on: Clustering tweets using K-Means for topic discovery

Afternoon Session

• Dimensionality Reduction for Text Data

  • Techniques: PCA, t-SNE, UMAP
  • Reducing high-dimensional text data for clustering and visualization
  • Hands-on: Applying PCA and t-SNE for visualizing topic models

• Hands-on Exercise

  • Topic modeling and clustering of product reviews using LDA and K-Means

Day 4: Advanced NLP Techniques & Transformers

Morning Session

• Introduction to Advanced NLP Techniques

  • Named Entity Recognition (NER), Part-of-Speech (POS) tagging
  • Dependency parsing and chunking
  • Hands-on: Performing NER on news articles using spaCy

• Transformers in NLP

  • Overview of BERT, GPT, T5, and other transformer models
  • Fine-tuning pre-trained models for specific tasks (text classification, summarization)
  • Hands-on: Fine-tuning BERT for sentiment analysis

Afternoon Session

• Text Summarization Techniques

  • Extractive vs. abstractive summarization
  • Hands-on: Implementing text summarization with BERT and GPT-3

• Hands-on Exercise

  • Building a chatbot using BERT for question-answering applications

Day 5: Real-World Applications & Deployment

Morning Session

• Text Mining in Business & Marketing

  • Sentiment analysis for customer feedback, brand monitoring
  • Text mining for market research and trend analysis
  • Hands-on: Analyzing customer feedback to improve product features

• Text Mining for Healthcare & Legal Use Cases

  • Mining medical records, clinical notes, and legal documents
  • Hands-on: Extracting structured data from unstructured medical reports

Afternoon Session

• Deploying Text Mining Solutions

  • Cloud-based solutions for text mining: AWS Comprehend, Google NLP API, Azure Cognitive Services
  • Real-time text mining applications using streaming data
  • Hands-on: Deploying an NLP model on AWS Lambda for real-time sentiment analysis

• Capstone Project & Final Presentations

  • Choose from:
    1. Building a text summarization tool for news articles
    2. Sentiment analysis and trend detection from social media feeds
    3. Clustering product reviews to uncover hidden trends
  • Participants present their projects & receive expert feedback

• Certification & Networking Session

Post-Course Benefits

• Hands-on experience with real-world text mining tools and techniques
• Expertise in text classification, sentiment analysis, and topic modeling
• Advanced NLP skills with state-of-the-art models (BERT, GPT, etc.)
• Real-world use case implementations across multiple domains
• Portfolio-ready projects to showcase your text mining capabilities