Text Analysis and NLP Tutorials

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Overview

This repository provides step-by-step tutorials on Text Analysis and Natural Language Processing (NLP). It is designed to guide learners and practitioners from foundational concepts to advanced techniques like transformer-based models. Each tutorial includes power point slides, code examples, and practical applications.

Topics

1. Text Preprocessing

• Tokenization
• Stemming and Lemmatization
• Stop-word Removal
• Handling special characters and punctuations
• Bag of Words Approach
• Vectorization
• TF-IDF

2. Dictionary Methods and Text Similarity

• Dictionary Methods
• Cosine Similarity
• Jaccard Index
• Euclidean Distance

3. Word Embeddings

• Understand word embeddings and their applications: Learn about Word2Vec, GloVe, and FastText.
• Use embeddings to calculate text similarity.
• Dimentionality: PCA and t-SNE

4. Text Classification with Traditional ML I

• Vectorizing text data using Bag-of-Words and TF-IDF
• Obtain text similarity and conduct regression analysis
• Building classifiers for text classification.
• Confusion matrix, metrics, and other model evaluation

5. Text Classification with Traditional ML II

• Regularization: LASSO, Ridge, Elastic Net
• Ensemble Learning: Boosting and Stacking
• Model optimization

6. Transformer-Based Models for NLP

• Understanding transformer architecture
• How transformer architecture revolutionized NLP
• Understand architectures like BERT, GPT, and RoBERTa.
• What can we achieve with Transformer-based models: Text classification, text generation, and semantic similarity.

7. Text Classification with LLMs

• Text classification with LLMs.
• Comparison of ML-based and transformer-based methods.
• Practical applications: classifying costomer complaints with BERT, RoBERTa, and T5.

8. Sentiment Analysis

• Lexicon-Based Methods: Using sentiment dictionaries like VADER and SentiWordNet.
• Machine Learning-Based Models: Training classifiers using labeled sentiment datasets.
• Transformer-Based Methods: Fine-tuning models like BERT for advanced sentiment prediction.
• Practical applications: social media analysis with Twitter-roBERTa-base-sentiment-latest.

9. Topic Modeling

• Latent Dirichlet Allocation (LDA)
• Structural Topic Models
• BERTopic

10. Named Entity Recognition (NER)

• Extract structured entities like names, dates, and locations from text.
• pre-trained spaCy models
• custom NER models using transformer-based architectures.

11. Custom NLP Pipelines from Hugging Face Models

• Build end-to-end NLP pipelines for real-world applications.
• Multi-step text processing workflows (e.g., preprocessing → classification → summarization).
• Integrating multiple NLP tasks into a single cohesive system.