Experiments on real-life emotions challenge Ekman's model.

Area of Science:

• Psychology
• Linguistics
• Computational Social Science

Background:

• Ekman's basic emotions theory posits universal emotional expressions.
• Alternative models view emotions as social and linguistic constructs.
• The sufficiency of current emotion models for real-life complexity is questioned.

Purpose of the Study:

• To assess the adequacy of traditional emotion models in capturing daily life emotions from text.
• To determine human-subject agreement rates using Ekman's model on annotated and unannotated datasets.
• To investigate the influence of alexithymia on emotion detection and categorization.

Main Methods:

• A social inquiry involving 114 subjects analyzing textual emotional content.
• Calculation of human-subject agreement rates for Ekman's theory (Entity-Level Tweets Emotional Analysis).
• Assessment of agreement using Ekman's model on sentences not adhering to the model (The Dictionary of Obscure Sorrows).
• Correlation analysis between alexithymia levels and emotion categorization accuracy.

Main Results:

• Low within-subject agreement rates were observed for both datasets.
• Subjects showed low agreement with original annotations.
• Individuals with high alexithymia frequently used Ekman's model, particularly for negative emotions.
• Low alexithymia levels were associated with particularly low agreement rates.

Conclusions:

• Traditional emotion models, like Ekman's, may be insufficient for capturing the nuances of real-life emotions in textual contexts.
• Alexithymia significantly influences the ability to accurately detect and categorize emotions.
• Further research is needed to develop more robust models for understanding complex human emotions.