Approaches to Analysing Historical Newspapers Using LLMs

2026-03-26

AI Summary

This study examines the divergent public discourses between conservative Catholic and liberal-progressive factions in late 19th- to early 20th-century Slovenian historical newspapers, focusing on collective identity, political stance, and national belonging, despite poor OCR quality. Integrating BERTopic for topic modeling, fine-grained sentiment analysis powered by the localized large language model GaMS3-12B-Instruct, named entity recognition, entity relation graph construction, network analysis, and critical discourse analysis, this work presents the first systematic evaluation and application of a Slovene large language model for sentiment classification in historical texts. The research not only uncovers shared thematic concerns and ideological divides between the two press camps but also demonstrates GaMS3-12B-Instruct’s superior performance in recognizing neutral sentiment and reveals distinct patterns in how different groups are represented across descriptive versus conflictual contexts.

Technology Category

• Natural Language Processing: Discourse, Pragmatics & Argument Mining
• Application Domains: Humanities & Computational Social Science
• Data Mining & Knowledge Management: Conversational Systems for Recommendation & Retrieval

Application Category

• Web Mining and Content Analysis: Sentiment analysis and opinion mining
• Search and Retrieval-Augmented AI: Multilingual and cross-lingual Web search
• Social Networks and Social Media: Media and governance, opinion dynamics, filter bubbles, polarization

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.

Problem

• historical newspapers
• collective identity
• sentiment analysis
• OCR-degraded text
• public discourse

Innovation

• LLM-based sentiment analysis
• historical newspaper analysis
• entity-graph visualization
• mixed methods digital humanities
• OCR-degraded text processing