Enhanced Keyword Extraction and Text Processing

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Enhanced Keyword Extraction and Text Processing

  • Day: 2025-02-17
  • Time: 15:20 to 15:50
  • Project: Dev
  • Workspace: WP 2: Operational
  • Status: Completed
  • Priority: MEDIUM
  • Assignee: Matías Nehuen Iglesias
  • Tags: Keyword_Extraction, Text_Processing, Python, TF-IDF, LDA

Description

Session Goal

The session aimed to enhance keyword extraction and text processing techniques, focusing on filtering excessive keywords, optimizing text cleaning, and resolving memory issues in Python.

Key Activities

  • Keyword Filtering and Selection: Explored methods using TF-IDF and LDA for refining keyword selection.
  • Spanish Stopwords in TfidfVectorizer: Implemented custom stopword lists using NLTK and spaCy to address limitations in Scikit-learn.
  • Text Processing Optimization: Structured a notebook for efficient text processing, including loading libraries, cleaning text, and applying topic modeling.
  • Text Cleaning Optimization: Planned improvements using regex and normalization for effective text cleaning.
  • Memory Issue Resolution: Addressed memory problems in Python with strategies for optimizing code and managing resources.

Achievements

  • Developed a comprehensive guide for keyword filtering and text processing.
  • Successfully implemented custom Spanish stopwords in TfidfVectorizer.
  • Created an optimized text processing workflow.
  • Identified and resolved memory issues in Python, enhancing code execution efficiency.

Pending Tasks

  • Further refine keyword extraction methods to improve accuracy.
  • Continue optimizing text cleaning processes for better performance.

Evidence

  • source_file=2025-02-17.sessions.jsonl, line_number=9, event_count=0, session_id=7faff9c0afcf456f6e809fc376e9461b98b3af3afe89bd5eec3815870c35bf7f
  • event_ids: []

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