Deriving and validating emotional dimensions from textual data

This paper proposes and analyzes a methodology for extracting the underlying emotional dimensions connected to different textual data, including social-media posts and online reviews. Our experiments result in a coherent conclusion across all 16 studied datasets. In particular, the found orthogonal emotional dimensions are a combination of valence (positive-negative sentiment), activation arousal (arousal-dominance), and expectancy tension (the intensity of the expectations concerning the future). We confirm the existence of both valence and arousal as core dimensions. On the other hand, dominance appears as an attribute connected to the variability of both valence and activation arousal dimensions. We also find some evidence for the existence of an ``unpredictability/novelty dimension discussed in recent academic work. Our key empirical contribution is that an additional orthogonal emotional dimension should be defined and named ``expectancy tension in that it captures the variability linked to the intensity of expectations regarding the future. Finally, our work contributes to the social computing literature by suggesting a novel methodology to derive emotional spaces from multiple textual data through eigenvector analyses.

Automated summary

This paper proposes and analyzes a methodology for extracting underlying emotional dimensions from different types of textual data. The study confirms the existence of valence and arousal as core emotional dimensions, and suggests that dominance is connected to the variability of both valence and arousal. Additionally, evidence is found for an "unpredictability/novelty dimension" discussed in recent academic work. The key contribution of this research is the identification of a new orthogonal emotional dimension named "expectancy tension," which captures the intensity of expectations regarding the future. This work contributes to the social computing literature by presenting a novel methodology for deriving emotional spaces from multiple textual data using eigenvector analyses.