📅 2025-02-17 — Session: Enhanced Keyword Extraction and Text Processing
🕒 15:20–15:50
🏷️ Labels: Keyword_Extraction, Text_Processing, Python, TF-IDF, LDA
📂 Project: Dev
⭐ Priority: MEDIUM
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.