Introduction to text analytics

• Overview of text analytics and its applications
• Introduction to Python libraries for text analytics (NLTK, spaCy, Gensim)

Text preprocessing techniques

• Tokenization, stemming, lemmatization, normalization, and stop word removal

Text Representation and Feature Extraction

• Bag-of-Words (BoW) model
• Term Frequency-Inverse Document Frequency (TF-IDF) representation
• Word embeddings using Word2Vec and GloVe

Text Classification

• Introduction to text classification
• Machine learning-based approaches for sentiment analysis
• Evaluation metrics for text classification

Text Classification

• Study case: sentiment analysis
• Hands on project in sentiment analysis

Token Classification

• Named Entity Recognition (NER)
• Part-of-Speech (POS) tagging
• Hands-on project: Implementing NER and POS tagging on text data

Topic Modeling

• Introduction to topic modeling
• Latent Dirichlet Allocation (LDA) model
• Topic visualization and interpretation
• Hands-on project

• Introduction to text visualization techniques
• Visualization libraries for text data in Python (e.g., Matplotlib, Seaborn, Plotly)
• Visualizing word frequency distributions, word clouds, and bar charts
• Interactive visualizations for exploring text data
• Hands-on project: Creating visualizations to represent insights derived from text analysis results.