Enhanced Email Data Analysis with NLP Techniques

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Enhanced Email Data Analysis with NLP Techniques

  • Day: 2025-03-01
  • Time: 03:05 to 04:00
  • Project: Dev
  • Workspace: WP 2: Operational
  • Status: Completed
  • Priority: MEDIUM
  • Assignee: Matías Nehuen Iglesias
  • Tags: NLP, Email Analysis, Keyword Extraction, NER, Dataframe

Description

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.

Evidence

  • source_file=2025-03-01.sessions.jsonl, line_number=6, event_count=0, session_id=6692c9713a669c76b5e82f6c0621c34893ebaa1e35d44a1fee7741d8826790a1
  • event_ids: []

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