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This module streamlines Python package management, script execution, file handling, web scraping, and multimedia downloads. It supports LLM-based NLP tasks like OCR, tokenization, lemmatization, POS tagging, NER, ATE, dependency parsing, MDD, WSD, LIWC, MIP analysis, text classification, and Chinese-English sentence alignment. Additionally, it generates word lists and data visualizations, making it a practical tool for data scraping and analysis—ideal for literary students and researchers.

Project description

Purpose: This module is designed to make complex tasks accessible and convenient, even for beginners. By providing a unified set of tools, it simplifies the workflow for data collection, processing, and analysis. Whether you're scraping data from the web, cleaning text, or performing LLM-based NLP tasks, this module ensures you can focus on your research without getting bogged down by technical challenges.

Key Features:

  1. Web Scraping: Easily scrape data from websites and download multimedia content.
  2. Package Management: Install, uninstall, and manage Python packages with simple commands.
  3. Data Retrieval: Extract data from various file formats like text, JSON, CSV, TSV, XLSX, XML, TMX, and HTML (both online and offline).
  4. Data Storage: Write and append data to text files, Excel, JSON, TMX, and JSON lines.
  5. File and Folder Processing: Manage file paths, create directories, move or copy files, convert CSV to JSON, and search for files with specific keywords.
  6. Data Cleaning: Clean text, handle punctuation, remove stopwords, detect noisy text, convert Markdown strings into Python objects, and prepare data for analysis, utilizing valuable corpora and dictionaries such as CET-4/6 vocabulary, BE21 and BNC-COCA word lists.
  7. NLP: Perform OCR, word tokenization, lemmatization, POS tagging, NER, dependency parsing, ATE, MDD, WSD, LIWC, MIP analysis, text classification, and Chinese-English sentence alignment using prepared LLM prompts.
  8. Math Operations: Format numbers, convert decimals to percentages, and validate data.
  9. Visualization: Process images (e.g., make white pixels transparent, resize images) and manage fonts for rendering text.

Author: Dr. Guisheng Pan (潘贵生) is an instructor at the School of Foreign Studies, Shanghai University of Finance and Economics (SUFE). Email: panguisheng@sufe.edu.cn Homepage: https://sfs.sufe.edu.cn/bf/ef/c4221a245743/page.htm

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