Answer: Apply a linear discriminant analysis.

The question requires implementing a feature engineering strategy for crowd sentiment local models where all shared features are continuous variables. Linear discriminant analysis (LDA) is the optimal choice because it is specifically designed for dimensionality reduction and feature transformation while preserving class discrimination. LDA works well with continuous variables and is commonly used in sentiment analysis scenarios to reduce feature space while maintaining predictive power. The community discussion supports D, with the highest upvoted comment noting that LDA is a dimensionality reduction method suitable for this context. While Pearson correlation (B) and Spearman correlation (C) are filter-based feature selection methods, they only measure relationships between individual features and the target, not providing comprehensive feature transformation. ANOVA (A) is primarily for testing group differences, not feature engineering. Given that all features are continuous and the goal is feature engineering (not just selection), LDA provides the most appropriate transformation strategy.

Author: LeetQuiz Editorial Team