Early Modern Rationalism NLP Dataset

Columns

• Auto-increment: A unique numerical identifier for each record.
• book_title: The title of the specific philosophical work (e.g., Discourse on the Method).
• publishing_date: The original year the work was published.
• authors: The name of the philosopher or author responsible for the work.
• text: The original, raw text content extracted from the source material.
• text_clean: A pre-processed version of the text that has been cleaned and tokenised for analysis.

Distribution

• Format: CSV (Comma Separated Values).
• Size: 3.06 MB.
• Structure: Structured tabular data containing 6 columns.
• Update Frequency: Weekly.

Usage

• Natural Language Processing (NLP): Ideal for training models on historical texts and philosophical discourse.
• Term Frequency Analysis: Analyse the prevalence of specific philosophical terms across different authors.
• Thematic Modelling: Create word clouds or topic models to identify core themes within Rationalism.
• Recommendation Systems: Build engines to suggest readings based on an evolving string of ideas.
• Historical Research: Compare the linguistic styles and arguments of Descartes, Spinoza, and Leibniz.

Coverage

• Geographic Scope: Primarily Western Europe (France, Netherlands, Germany).
• Time Range: 1637 to 1710.
• Demographic Scope: Key figures of the Rationalist school of philosophy.
• Data Availability: Includes major works such as Discourse on the Method.

License

Who Can Use It

• Data Scientists: For testing NLP algorithms and cleaning functions on unstructured text.
• Philosophers and Historians: For digital humanities research and textual analysis.
• Students: For educational projects involving text mining or the history of philosophy.
• Linguists: For studying the evolution of language in early modern philosophical texts.

Dataset Name Suggestions

• Rationalist Masters Corpus
• Descartes Spinoza Leibniz Text Collection
• Early Modern Rationalism NLP Dataset
• Cleaned Rationalist Philosophy Texts

Attributes