Overview
Use Python and Google CoLab For Social Media Mining and Text Analysis and Natural Language Processing (NLP)
Syllabus
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- Introduction to Text Analysis and NLP
-- Overview of Natural Language Processing
-- Importance of Text Analysis in Modern Applications
-- Python for NLP: An Overview
- Setting Up the Python Environment
-- Installing Python and IDEs
-- Utilizing Jupyter Notebooks
-- Key Libraries: NLTK, spaCy, scikit-learn
- Basic Text Processing Techniques
-- Tokenization
-- Stopwords Removal
-- Regular Expressions
- Text Preprocessing
-- Case Normalization
-- Stemming and Lemmatization
-- Handling Punctuation, Numbers, and Symbols
- Feature Extraction
-- Bag of Words Model
-- Term Frequency-Inverse Document Frequency (TF-IDF)
-- Word Embeddings: Word2Vec, GloVe
- Sentiment Analysis
-- Introduction to Sentiment Analysis
-- Using NLTK for Sentiment Analysis
-- Building a Sentiment Classifier with scikit-learn
- Text Classification
-- Understanding Text Classification
-- Supervised Machine Learning for Text
-- Building and Evaluating Classifiers
- Advanced Topics in NLP
-- Named Entity Recognition (NER)
-- Topic Modeling with LDA
-- Introduction to Transformer Models and BERT
- Social Media Data Analysis
-- Collecting Data from Social Media APIs
-- Text Analysis in Social Media Contexts
-- Case Studies and Applications
- Practical Projects and Case Studies
-- Sentiment Analysis on Twitter Data
-- Building a Chatbot
-- Text Classification for News Articles
- Conclusion and Further Learning
-- Recap of Key Concepts
-- Resources for Continued Learning in NLP
-- Final Project Presentation and Feedback
Taught by
Tags
On-Demand
