The Psychology of Semantic Spaces: Experiments with Positive Emotion (Student Abstract)

Authors

  • Xuan Liu University of California Berkeley
  • Kokil Jaidka National University of Singapore
  • Niyati Chayya Big Data Experience Lab, Adobe Research

DOI:

https://doi.org/10.1609/aaai.v36i11.21640

Keywords:

Happiness, Emotion, Computational Linguistics, Bert, Transformers

Abstract

Psychological concepts can help computational linguists to better model the latent semantic spaces of emotions, and understand the underlying states motivating the sharing or suppressing of emotions. This abstract applies the understanding of agency and social interaction in the happiness semantic space to its role in positive emotion. First, BERT-based fine-tuning yields an expanded seed set to understand the vocabulary of the latent space. Next, results benchmarked against many emotion datasets suggest that the approach is valid, robust, offers an improvement over direct prediction, and is useful for downstream predictive tasks related to psychological states.

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Published

2022-06-28

How to Cite

Liu, X., Jaidka, K., & Chayya, N. (2022). The Psychology of Semantic Spaces: Experiments with Positive Emotion (Student Abstract). Proceedings of the AAAI Conference on Artificial Intelligence, 36(11), 13007-13008. https://doi.org/10.1609/aaai.v36i11.21640