نویسنده
Kafescioglu, N., Yildiz, Olcay Taner, Demiroglu, Cenk, Polat, Esma Nafiye
تاریخ انتشار
2024-01-01
محل انتشار
-
IEEE
موضوع
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
نوع
دوره ای
زبان
انگلیسی
دیجیتال
بله
نسخه خطی
خیر
کتابخانه
دانشگاه اوزیغین
شناسه دارایی کتابخانه
2169-3536
شماره ثبت
ef72c83c-1425-43fa-a15b-0bd5d177da35
محل کتابخانه
Computer Science
تاریخ
2024-01-01
یادداشتها
TÜBİTAK
متن نمونه
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
