Abstract
This study presents a computational analysis of the Slovene historical newspapers \textit{Slovenec} and \textit{Slovenski narod} from the sPeriodika corpus, combining topic modelling, large language model (LLM)-based aspect-level sentiment analysis, entity-graph visualisation, and qualitative discourse analysis to examine how collective identities, political orientations, and national belonging were represented in public discourse at the turn of the twentieth century. Using BERTopic, we identify major thematic patterns and show both shared concerns and clear ideological differences between the two newspapers, reflecting their conservative-Catholic and liberal-progressive orientations. We further evaluate four instruction-following LLMs for targeted sentiment classification in OCR-degraded historical Slovene and select the Slovene-adapted GaMS3-12B-Instruct model as the most suitable for large-scale application, while also documenting important limitations, particularly its stronger performance on neutral sentiment than on positive or negative sentiment. Applied at dataset scale, the model reveals meaningful variation in the portrayal of collective identities, with some groups appearing predominantly in neutral descriptive contexts and others more often in evaluative or conflict-related discourse. We then create NER graphs to explore the relationships between collective identities and places. We apply a mixed methods approach to analyse the named entity graphs, combining quantitative network analysis with critical discourse analysis. The investigation focuses on the emergence and development of intertwined historical political and socionomic identities. Overall, the study demonstrates the value of combining scalable computational methods with critical interpretation to support digital humanities research on noisy historical newspaper data.
Abstract (translated)
本研究对sPeriodika语料库中的斯洛文尼亚历史报纸《Slovenec》与《Slovenski narod》进行了计算分析，综合运用主题建模、基于大语言模型（LLM）的方面级情感分析、实体图可视化及质性话语分析，探究20世纪之交公共话语中集体身份、政治倾向与国家归属的呈现方式。通过BERTopic识别主要主题模式，揭示两份报纸在保守-天主教与自由-进步取向下的共同关切与明显意识形态差异。研究还评估了四种指令遵循型大语言模型对光学字符识别（OCR）受损的历史斯洛文尼亚语文本进行定向情感分类的效果，选定经斯洛文尼亚语优化的GaMS3-12B-Instruct模型作为大规模应用的最佳选择，同时记录其重要局限——该模型在中性情感识别上表现显著优于积极或消极情感。在数据集层面应用该模型发现，集体身份的 portrayal 存在显著差异：部分群体主要出现在中性描述语境中，而另一些则更频繁出现在评价性或冲突相关话语里。随后构建命名实体图以探索集体身份与地点的关联，采用混合方法分析实体图，结合量化网络分析与批判性话语分析，聚焦历史政治与社会经济身份的交织演进。整体而言，本研究展示了将可扩展计算方法与批判性阐释相结合的价值，为处理噪声较大的历史报纸数据的数字人文研究提供了支持。
URL
https://arxiv.org/abs/2603.25051
