identify key predictors influencing disease outcomes and evaluate the diagnostic accuracy

Part 1: Traditional Tidy Text Approach Uses tidytext package with lexicon-based methods (AFINN, Bing, NRC) Word-by-word sentiment scoring Effective for literary text analysis 25 Part 2: Advanced Sentiment Analysis (Pride and Prejudice) Employs multi-dimensional emotion analysis using NRC lexicon to track eight distinct emotions (joy, anger, fear, trust, anticipation, surprise, sadness, disgust) across the narrative arc Implements context-aware sentiment scoring with sentimentr package, which accounts for valence shifters like negations (“not happy”) and amplifiers (“very good”) for more nuanced analysis Includes character-specific sentiment tracking to analyze how emotional tone shifts when major characters (Elizabeth, Darcy, Wickham) are mentioned, revealing character development patterns Compares three distinct lexicons (AFINN, Bing, NRC) at both chapter and sentence levels to demonstrate methodological rigor and validate findings across different sentiment

We train 3 binary logistic regression models to classify weather a neighborhood would be classified as highcrime rate with a given set of predictors.

Código que forma parte de las evidencias para la práctica 2 del módulo 6 del diplomado en Técnicas Estadísticas y Minería de Datos