Text Mining Oral Histories in Historical Archaeology.

Area of Science:

• Digital Humanities
• Historical Archaeology
• Computational Linguistics

Background:

• Text mining and natural language processing (NLP) offer powerful tools for analyzing large datasets.
• Current text mining methods are primarily designed for contemporary, publicly available texts, limiting their direct application to historical and archaeological research.
• Historical and archaeological data often involve unique challenges, such as digitized historical documents and specialized vocabularies.

Purpose of the Study:

• To explore the potential of text mining and NLP methodologies for historical archaeology and oral history research.
• To address the limitations of current text mining tools when applied to historical and place-based texts.
• To propose pathways for adapting advanced text analysis techniques for historical research contexts.

Main Methods:

• Utilizing recent methodological developments in text analysis.
• Applying text analysis to oral histories from the anthracite coal mining region of Pennsylvania, recorded approximately 50 years ago.
• Examining the adaptation of generalized text mining methods for historical and place-based textual data.

Main Results:

• Text analysis demonstrates significant potential for informing historical archaeological research, especially with digitized repositories and lengthy texts.
• The study highlights the need for tailored approaches to bridge the gap between general text mining and specific historical data requirements.
• Oral history data can be effectively analyzed using adapted text mining techniques.

Conclusions:

• Advanced text mining and NLP methodologies can be productively applied to historical archaeology and oral history.
• Methodological adaptations are necessary to overcome the challenges posed by historical and place-based texts.
• Future research should focus on developing and refining text analysis tools for specialized historical research applications.