Decoding emotional dynamics: A comparative analysis of contextual and non-contextual models in sentiment analysis of turkish couple dialogues

Name: Decoding emotional dynamics: A comparative analysis of contextual and non-contextual models in sentiment analysis of turkish couple dialogues
Author: Kafescioglu, N., Yildiz, Olcay Taner, Demiroglu, Cenk, Polat, Esma Nafiye

İsim	Decoding emotional dynamics: A comparative analysis of contextual and non-contextual models in sentiment analysis of turkish couple dialogues
Yazar	Kafescioglu, N., Yildiz, Olcay Taner, Demiroglu, Cenk, Polat, Esma Nafiye
Basım Tarihi:	2024-01-01
Basım Yeri	- IEEE
Konu	Turkish language, Non-contextual model, Morphological analysis, Llms, Llama 2, Gpt, Fine tuning, Dialoguernn, Couple dialogue dataset, Conversational sentiment analysis, Contextual model, Context modeling, Decoding, Benchmark testing, Oral communication, Large language models, Analytical models, Sentiment analysis
Tür	Süreli Yayın
Dil	İngilizce
Dijital	Evet
Yazma	Hayır
Kütüphane:	Özyeğin Üniversitesi
Demirbaş Numarası	2169-3536
Kayıt Numarası	ef72c83c-1425-43fa-a15b-0bd5d177da35
Lokasyon	Computer Science
Tarih	2024-01-01
Notlar	TÜBİTAK
Örnek Metin	This 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.
DOI	10.1109/ACCESS.2024.3496867
Cilt	12

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Decoding emotional dynamics: A comparative analysis of contextual and non-contextual models in sentiment analysis of turkish couple dialogues

Yazar Kafescioglu, N., Yildiz, Olcay Taner, Demiroglu, Cenk, Polat, Esma Nafiye

Basım Tarihi 2024-01-01

Basım Yeri - IEEE

Konu Turkish language, Non-contextual model, Morphological analysis, Llms, Llama 2, Gpt, Fine tuning, Dialoguernn, Couple dialogue dataset, Conversational sentiment analysis, Contextual model, Context modeling, Decoding, Benchmark testing, Oral communication, Large language models, Analytical models, Sentiment analysis

Tür Süreli Yayın

Dil İngilizce

Dijital Evet

Yazma Hayır

Kütüphane Özyeğin Üniversitesi

Demirbaş Numarası 2169-3536

Kayıt Numarası ef72c83c-1425-43fa-a15b-0bd5d177da35

Lokasyon Computer Science

Tarih 2024-01-01

Notlar TÜBİTAK

Örnek Metin This 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.

DOI 10.1109/ACCESS.2024.3496867

Cilt 12