Decoding Emotional Dynamics: A Comparative Analysis of Contextual and Non-Contextual Models in Sentiment Analysis of Turkish Couple Dialogues

Basit Öğe Kaydı
dc.authorid0000-0001-5838-4615
dc.authorid0000-0001-7683-240X
dc.contributor.authorPolat, Esma Nafiye
dc.contributor.authorDemiroglu, Cenk
dc.contributor.authorYildiz, Olcay Taner
dc.contributor.authorKafescioglu, Nilufer
dc.date.accessioned2026-04-04T18:55:50Z
dc.date.available2026-04-04T18:55:50Z
dc.date.issued2024
dc.departmentİstanbul Bilgi Üniversitesi
dc.description.abstractThis paper introduces the Couple Dialogue dataset, specifically curated for conversational sentiment analysis in the Turkish language. It comprises 14,294 utterances from 118 dyadic conversations between couples, each annotated to capture sentiment transitions and interpersonal dynamics. Our study contrasts two distinct modeling frameworks-the Non-Contextual Model and the Contextual Model. The Non-Contextual Model analyzes utterances independently, typically overlooking the nuances of conversational dynamics and sentiment evolution. Within this framework, we conducted a detailed morphological analysis due to the agglutinative nature and rich morphological structure of the Turkish language, which included various word forms and negation morphemes crucial for sentiment representation. In contrast, the Contextual Model employs state-of-the-art Large Language Models (LLMs) such as BERT, GPT-3.5 Turbo, Llama 2, and GPT-4, alongside architectures like DialogueRNN. This model processes the sequential and relational aspects of dialogues through three approaches: prompt-based, fine-tuned, and embedding-based methods, particularly enhanced with fine-tuning and advanced embedding techniques (utilizing pre-trained and fine-tuned Turkish BERT). The Contextual Model substantially outperforms the Non-Contextual Model, showing a 9.98% improvement in Weighted F1 scores validated by statistical tests. Our work not only pioneers the use of LLMs in Turkish conversational sentiment analysis but also underscores the critical importance of contextual understanding in capturing complex emotional cues in couple interactions. This study sets a robust benchmark for future explorations into sentiment analysis within linguistically rich contexts.
dc.description.sponsorshipTUBITAK 1001 Program (The Scientific and Research Council of Turkey) [113K538]
dc.description.sponsorshipThe data of this study were collected in a project funded by TUBITAK 1001 Program (The Scientific and Research Council of Turkey; Project No: 113K538).
dc.identifier.doi10.1109/ACCESS.2024.3496867
dc.identifier.doi10.1109/ACCESS.2024.3496867
dc.identifier.endpage172695
dc.identifier.issn2169-3536
dc.identifier.scopus2-s2.0-85210396336
dc.identifier.scopusqualityQ1
dc.identifier.startpage172648
dc.identifier.urihttps://doi.org/10.1109/ACCESS.2024.3496867
dc.identifier.urihttps://hdl.handle.net/11411/10577
dc.identifier.volume12
dc.identifier.wosWOS:001409546700014
dc.identifier.wosqualityQ2
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherIeee-Inst Electrical Electronics Engineers Inc
dc.relation.ispartofIeee Access
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_WoS_20260402
dc.snmzKA_Scopus_20260402
dc.subjectSentiment Analysis
dc.subjectAnalytical Models
dc.subjectLarge Language Models
dc.subjectOral Communication
dc.subjectBenchmark Testing
dc.subjectDecoding
dc.subjectContext Modeling
dc.subjectContextual Model
dc.subjectConversational Sentiment Analysis
dc.subjectCouple Dialogue Dataset
dc.subjectDialoguernn
dc.subjectFine Tuning
dc.subjectGpt
dc.subjectLlama 2
dc.subjectLlms
dc.subjectMorphological Analysis
dc.subjectNon-Contextual Model
dc.subjectTurkish Language
dc.titleDecoding Emotional Dynamics: A Comparative Analysis of Contextual and Non-Contextual Models in Sentiment Analysis of Turkish Couple Dialogues
dc.typeArticle

Dosyalar

Koleksiyon

Web of Science Indexed Publications
Scopus Indexed Publications