📅 2023-11-19 — Session: Implemented NLP techniques for semantic analysis
🕒 17:15–19:30
🏷️ Labels: NLP, Python, Semantic Analysis, Text Processing, Spacy
📂 Project: Dev
⭐ Priority: MEDIUM
Session Goal
This session focused on implementing Natural Language Processing (NLP) techniques for semantic analysis, specifically targeting the context of the word ‘docentes’.
Key Activities
- Exporting Word Counts: A Python script was used to export word count data to a CSV file using the pandas library.
- Text Processing: Replaced ellipses in text processing scripts to ensure proper word separation.
- NLP Planning: Discussed and outlined NLP techniques such as sentiment analysis, topic modeling, and named entity recognition for analyzing speeches and semantic understanding.
- Semantic Analysis Preparation: Prepared a text file ‘docentes_context.txt’ for semantic analysis and developed a Python script to extract contexts of the word ‘docentes’.
- POS Tagging Strategy: Developed a strategy using Part-of-Speech tagging to analyze contexts of ‘docentes’ using spaCy.
- Language Model Installation: Installed the Spanish language model for spaCy to facilitate NLP tasks in Spanish.
Achievements
- Successfully exported word count data to CSV format.
- Developed scripts for text and semantic analysis, focusing on the term ‘docentes’.
- Installed necessary NLP tools and models for Spanish language processing.
Pending Tasks
- Further analysis of the extracted contexts using the installed NLP models and techniques.
- Implementation of sentiment analysis and topic modeling on the ‘docentes’ contexts.
- Evaluation of the effectiveness of the NLP strategies implemented.