Decoding Emotional Experiences in Dyadic Conversations of Married
Couples: Leveraging Semantic Similarity through Sentence Embedding
- URL: http://arxiv.org/abs/2309.12646v2
- Date: Sun, 25 Feb 2024 19:43:20 GMT
- Title: Decoding Emotional Experiences in Dyadic Conversations of Married
Couples: Leveraging Semantic Similarity through Sentence Embedding
- Authors: Chen-Wei Yu, Yun-Shiuan Chuang, Alexandros N. Lotsos, and Claudia M.
Haase
- Abstract summary: The present study analyzes verbal conversations of 50 married couples who engage in naturalistic 10-minute conflict and 10-minute positive conversations.
Transformer-based model General Text Embeddings-Large is employed to obtain the embeddings of the utterances from each speaker.
Results show that lower similarity is associated with greater positive emotional experiences in the positive (but not conflict) conversation.
- Score: 41.94295877935867
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Recent advancements in Natural Language Processing (NLP) have highlighted the
potential of sentence embeddings in measuring semantic similarity (hereafter
similarity). Yet, whether this approach can be used to analyze real-world
dyadic interactions and predict people's emotional experiences in response to
these interactions remains largely uncharted. To bridge this gap, the present
study analyzes verbal conversations of 50 married couples who engage in
naturalistic 10-minute conflict and 10-minute positive conversations.
Transformer-based model General Text Embeddings-Large is employed to obtain the
embeddings of the utterances from each speaker. The overall similarity of the
conversations is then quantified by the average cosine similarity between the
embeddings of adjacent utterances. Results show that lower similarity is
associated with greater positive emotional experiences in the positive (but not
conflict) conversation. Follow-up analyses show that (a) findings remain stable
when controlling for marital satisfaction and the number of utterance pairs and
(b) the similarity measure is valid in capturing critical features of a dyadic
conversation. The present study underscores the potency of sentence embeddings
in understanding links between interpersonal dynamics and individual emotional
experiences, paving the way for innovative applications of NLP tools in
affective and relationship science.
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