📅 2025-03-01 — Session: Enhanced Email Data Analysis with NLP Techniques
🕒 03:05–04:00
🏷️ Labels: NLP, Email Analysis, Keyword Extraction, NER, Dataframe
📂 Project: Dev
⭐ Priority: MEDIUM
Session Goal
The session aimed to enhance email data analysis using advanced Natural Language Processing (NLP) techniques, focusing on improving keyword and named entity extraction.
Key Activities
- DataFrame Filtering: Implemented Python code to filter email data by sender and receiver using Pandas.
- Email Data Insights: Analyzed email exchanges to derive insights on collaboration and communication patterns.
- LDA-Based Keyword Extraction: Applied Latent Dirichlet Allocation (LDA) for extracting keywords from emails, including preprocessing and visualization.
- RAKE Optimization: Improved RAKE keyword extraction to reduce irrelevant metadata capture.
- Named Entity Recognition (NER): Developed a SpaCy-based NER function to identify entities in email bodies, and explored predefined entity types.
- NER Performance Enhancement: Discussed strategies to improve NER accuracy, including token cleaning and custom filtering.
- Advanced NER Models: Evaluated transformer-based models like BERT and RoBERTa for potential use in specialized NER tasks.
Achievements
- Successfully implemented and tested multiple NLP techniques for email analysis.
- Improved the quality of keyword and entity extraction processes.
- Identified potential enhancements for LDA and RAKE methods.
Pending Tasks
- Further refine LDA and RAKE models to enhance topic and keyword extraction.
- Explore the integration of advanced NER models for domain-specific applications.