Data Extraction from Montesquieu's Persian Letters and Code for the Article "Literary study of the most common objects through a statistical AI-driven analysis of Montesquieu’s Persian Letters"
Description
This dataset is supplementary to the article "Literary study of the most common objects through a statistical AI-driven analysis of Montesquieu’s Persian Letters" and includes comprehensive processes and results of extracting, processing, and analyzing the letters through the integration of digital humanities and traditional literary analysis. The data presented in this dataset includes: Table S1: Final Letter Categorization: This table contains the final categorization of 161 letters from Persian Letters based on literary features, as extracted from a Large Language Model (LLM) API. Table S1 Contents includes: 1. Letter Number: Unique identifier for each letter. 2. Sender: The individual or entity who sent the letter. 3. Receiver: The individual or entity who received the letter. 3. Object: The primary subject or focus of the letter. 4. Theme: The underlying theme or message conveyed in the letter. 5. Mode: The mode of communication or expression used in the letter. 6. Tone: The emotional tone or attitude present in the letter. 7. Comparative Mode: the comparative communication mode across different argumentations and ideas. 8. Character Count: The total number of characters in each letter. 9. Object First Word: The first word of the primary object in the letter. Code.Word: Workflow Documentation: This file details the processes involved in extracting, processing, and analyzing letters from Persian Letters. It includes Python scripts and workflows covering: Extracting Letter Titles Combining Letters into a DataFrame Character and Word Count Analysis Generating Prompts and Saving Responses Parsing Responses to Extract Answers Visualization and Analysis This dataset supports the main article by providing new insights into Montesquieu's narrative techniques and thematic explorations within Persian Letters. It demonstrates how digital tools like natural language processing (NLP) and data analysis can uncover deeper meanings in classic literary works, offering new perspectives on the structure and narrative strategies employed by Montesquieu through his work.