πŸ“… 2023-11-19 β€” Session: Implemented NLP techniques for semantic analysis

πŸ•’ 17:15–19:30
🏷️ Labels: NLP, Python, Text Processing, Semantic Analysis, Spacy
πŸ“‚ Project: Dev
⭐ Priority: MEDIUM

Session Goal:

The session aimed to implement and explore various Natural Language Processing (NLP) techniques for semantic analysis, focusing on text processing and analysis of the term β€˜docentes’.

Key Activities:

  • Exporting Word Counts to CSV: Utilized Python and the pandas library to convert word counts into a DataFrame and export them to a CSV file.
  • Text Processing: Replaced ellipses in text to ensure proper word separation.
  • NLP Techniques Overview: Reviewed key NLP methods such as sentiment analysis, topic modeling, and named entity recognition for analyzing speeches.
  • Semantic Analysis Framework: Developed a framework for semantic understanding using part-of-speech tagging and contextual word relationships.
  • Text File Preparation: Created a text file β€˜docentes_context.txt’ for semantic analysis.
  • Python Script Execution: Implemented a script to extract sentences containing β€˜docentes’ for context analysis.
  • POS Tagging Strategy: Planned a strategy for POS tagging using spaCy for analyzing β€˜docentes’ contexts.
  • Spanish Language Model Installation: Installed the es_core_news_sm Spanish language model for spaCy to facilitate NLP tasks in Spanish.

Achievements:

  • Successfully exported word counts to CSV and processed text for analysis.
  • Gained insights into NLP techniques applicable to semantic analysis.
  • Prepared necessary text files and scripts for detailed analysis of β€˜docentes’.

Pending Tasks:

  • Implement the POS tagging strategy using spaCy on the β€˜docentes’ contexts.
  • Conduct detailed semantic analysis using the installed Spanish language model in spaCy.