π 2023-11-19 β Session: Implemented NLP techniques for semantic analysis
π 17:15β19:30
π·οΈ Labels: NLP, Python, Text Processing, Semantic Analysis, Spacy
π Project: Dev
β 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 βdocentesβ.
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 βdocentes_context.txtβ for semantic analysis.
- Python Script Execution: Implemented a script to extract sentences containing βdocentesβ for context analysis.
- POS Tagging Strategy: Planned a strategy for POS tagging using spaCy for analyzing βdocentesβ 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 βdocentesβ.
Pending Tasks:
- Implement the POS tagging strategy using spaCy on the βdocentesβ contexts.
- Conduct detailed semantic analysis using the installed Spanish language model in spaCy.