Affective, semantic, frequency, and descriptive norms for 107 face emojis
Tatjana Scheffler1, Ivan Nenchev2
1Department for German Language and Literature, Ruhr University Bochum, Universitätsstraße 150, 44801, Bochum, Germany. tatjana.scheffler@rub.de.
Behavior Research Methods
|August 15, 2024
Summary
This study presents a new dataset of facial emoji norms, including emotional ratings and usage frequency. Emoji familiarity correlates with usage and clearer meanings, enhancing semantic understanding in digital communication.
Area of Science:
- Psycholinguistics
- Computational Linguistics
- Human-Computer Interaction
Background:
- Emojis are increasingly prevalent in digital communication.
- Existing emoji datasets lack comprehensive affective and semantic norms.
- Understanding emoji meaning is crucial for cognitive and linguistic research.
Purpose of the Study:
- To create a novel dataset of affective, semantic, and descriptive norms for facial emojis.
- To analyze subjective ratings and usage frequencies of emojis.
- To develop description-based emoji vector embeddings for semantic analysis.
Main Methods:
- Collected subjective ratings (valence, arousal, familiarity, clarity, visual complexity) from 138 German speakers for all facial emojis.
- Gathered emoji usage frequency data from Twitter and WhatsApp corpora.
- Generated vector embeddings from participant-provided emoji descriptions.
Main Results:
- Replicated the quadratic relationship between arousal and valence for emojis, similar to words.
- Found strong correlations between emoji familiarity, usage frequency, emotional valence, and clarity.
- Description-based emoji vector embeddings effectively capture semantic relationships and surpass existing models.
Conclusions:
- The developed emoji norm dataset offers high reliability and validity.
- This resource will advance research on the cognitive processing and linguistic properties of emojis.
- The findings have implications for controlled experimental design and the future of emoji research.
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