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This lecture delves into understanding mood expressed on Twitter, where positive affect (PA) and negative affect (NA) are independent dimensions. The goal is to study variations in PA & NA over time, day of week, and world region using longitudinal Twitter data. The lecture covers the extraction of PA and NA, the use of LIWC2015 for text analysis, and visualizing Twitter mood. It also explores hourly changes in individual affect in different regions and emphasizes the importance of considering biased data in large-scale human behavior analysis.