Mustafa İlter; Yasemin Özcan Gönülal; Buket Erşahin; Doğan Evecen; Sezen Karabulut; Selma Tekir; Emre Onuç; İbrahim Berci Cross-individual sentiment analysis for historical text processing: detecting author-based relationships in Ottoman-Turkish memoirs Journal Article Digital Scholarship in the Humanities, 41 (3), pp. 1383-1400, 2026, ISSN: 2055-7671. Abstract | Links | BibTeX @article{10.1093/llc/fqag063,
title = {Cross-individual sentiment analysis for historical text processing: detecting author-based relationships in Ottoman-Turkish memoirs},
author = {Mustafa \.{I}lter and Yasemin \"{O}zcan G\"{o}n\"{u}lal and Buket Er\c{s}ahin and Do\u{g}an Evecen and Sezen Karabulut and Selma Tekir and Emre Onu\c{c} and \.{I}brahim Berci},
url = {https://doi.org/10.1093/llc/fqag063},
doi = {10.1093/llc/fqag063},
issn = {2055-7671},
year = {2026},
date = {2026-01-01},
journal = {Digital Scholarship in the Humanities},
volume = {41},
number = {3},
pages = {1383-1400},
abstract = {Sentiment analysis in digital humanities can reveal interpersonal relationships from large amounts of data across different texts at the same time. This study aims to automatically detect authors’ sentiments toward individuals mentioned in Late Ottoman\textemdashEarly Turkish Republic period memoirs. We focused on two staged pipeline which allows understanding authors’ relationships with other individual personalities. We first fine-tuned BERTurk model for Named Entity Recognition (NER) task to detect individuals in memoirs. Secondly, while the literature acknowledges the challenges of sentiment analysis in historical and literary texts, we further endeavor to detect not only the general sentiments of the given text but also authors’ sentiments toward mentioned individuals. To address this, we experimentally explored possible ways to identify authors’ sentiments toward individuals, namely cross-individual sentiment analysis (CISA), by fine-tuning encoder-based PLMs. Our general sentiment analysis model achieved an F1 score of 0.9262, and our CISA pipeline achieved 0.8705. The framework from NER to sentiment analysis revealed promising results for such tasks, as shown in excerpts from \.{I}brahim Temo’s memoir, subsequently offering the field of digital humanities a framework to analyse interpersonal relationships within large corpora of historical texts.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Sentiment analysis in digital humanities can reveal interpersonal relationships from large amounts of data across different texts at the same time. This study aims to automatically detect authors’ sentiments toward individuals mentioned in Late Ottoman—Early Turkish Republic period memoirs. We focused on two staged pipeline which allows understanding authors’ relationships with other individual personalities. We first fine-tuned BERTurk model for Named Entity Recognition (NER) task to detect individuals in memoirs. Secondly, while the literature acknowledges the challenges of sentiment analysis in historical and literary texts, we further endeavor to detect not only the general sentiments of the given text but also authors’ sentiments toward mentioned individuals. To address this, we experimentally explored possible ways to identify authors’ sentiments toward individuals, namely cross-individual sentiment analysis (CISA), by fine-tuning encoder-based PLMs. Our general sentiment analysis model achieved an F1 score of 0.9262, and our CISA pipeline achieved 0.8705. The framework from NER to sentiment analysis revealed promising results for such tasks, as shown in excerpts from İbrahim Temo’s memoir, subsequently offering the field of digital humanities a framework to analyse interpersonal relationships within large corpora of historical texts. |
Mustafa Erşahin; Semih Utku; Deniz Kilinc; Buket Erşahin Information retrieval-based bug localization approach with adaptive attributeweighting Journal Article Turkish Journal of Electrical Engineering and Computer Sciences, 29 (3), pp. 1598–1614, 2021. BibTeX @article{ercsahin2021information,
title = {Information retrieval-based bug localization approach with adaptive attributeweighting},
author = { Mustafa Er\c{s}ahin and Semih Utku and Deniz Kilinc and Buket Er\c{s}ahin},
year = {2021},
date = {2021-01-01},
journal = {Turkish Journal of Electrical Engineering and Computer Sciences},
volume = {29},
number = {3},
pages = {1598--1614},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
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