Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
Permanent URI for this collectionhttps://hdl.handle.net/11147/7148
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Conference Object Adapting Language Models to Sentiment Analysis for Automatically Translated and Labelled Turkish News Texts(Institute of Electrical and Electronics Engineers Inc., 2025) Serficeli, S.C.; Udunman, B.; Inan, E.The proliferation of news sources makes it difficult to track current events and social events in real time. In order to interpret social events in this context quickly and effectively, it is important to translate news texts provided in different natural languages into Turkish and to perform sentiment analysis on them. The aim of this study is to translate multilingual news texts into Turkish and perform sentiment analysis on these texts. The generated labels were compared and the data that were given the same label by all models were separated as automatically labelled data. This automatic labelling process ensured that the data for which different models produced consistent results were reliably labelled. When the results were evaluated, F1 score of 0.946 was achieved for sentiment analysis using the automatic labelling mechanism for texts translated into Turkish. © 2025 IEEE.Conference Object Improvements on a Multi-Task Bert Model(Ieee, 2024) Agrali, Mahmut; Tekir, SelmaPre-trained language models have introduced significant performance boosts in natural language processing. Fine-tuning of these models using downstream tasks' supervised data further improves the acquired results. In the fine-tuning process, combining the learning of tasks is an effective approach. This paper proposes a multi-task learning framework based on BERT. To accomplish the tasks of sentiment analysis, paraphrase detection, and semantic text similarity, we include linear layers, a Siamese network with cosine similarity, and convolutional layers to the appropriate places in the architecture. We conducted an ablation study using Stanford Sentiment Treebank (SST), Quora, and SemEval STS datasets for each task to test the framework and its components' effectiveness. The results demonstrate that the proposed multi-task framework improves the performance of BERT. The best results obtained for sentiment analysis, paraphrase detection, and semantic text similarity are accuracies of 0.534 and 0.697 and a Pearson correlation coefficient of 0.345.