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) was suggested by one commenter, it's primarily for feature selection rather than comprehensive feature engineering, and ANOVA (A) is more suited for hypothesis testing rather than feature transformation. Spearman correlation (C) is designed for ordinal/non-parametric data, which doesn't align with the continuous variable requirement.

Author: LeetQuiz Editorial Team