This study builds upon a previous research on classifying posts in discussion forums based on their role within threads. First, we extend Bhatia et al.’s model by incorporating additional features related to content, structure, user behavior, and sentiment, and evaluate the enhanced model on two datasets: Ubuntu Forum (UB) and TripAdvisor New York (NYC), showing improved classification accuracy, particularly in social forums, where sentiment features played a significant role. Later, we refine the previous model by introducing a feature ranking approach based on Chi-square values, to identify the most influential variables for classification. This analysis determines which features contribute most to the model’s performance, enabling a more optimized and efficient classification process. This work not only improves classification accuracy but also introduces a methodological advancement through the application of Chi-square analysis for feature selection. By identifying and prioritizing the most impactful features, we provide a more efficient and interpretable framework for classifying posts in online discussion forums.