Related papers: Decoding Emotional Experiences in Dyadic Conversations of Married Couples: Leveraging Semantic Similarity through Sentence Embedding

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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