Text Mining Analysis for Lessons Learned Database
Budget: $30 – $250 USD
Project Overview
I am seeking a skilled data analyst with expertise in text mining to extract and analyze specific information from a large list containing Lessons Learned records (approx 700 items). The database is in Excel format and consists of technical text entries that require a structured analysis.
Scope of Work:
The objective of this project is to conduct a technical analysis of Lessons Learned data, with a focus on understanding the key issues, identifying root causes, and categorizing insights. The analysis should include:
Data Understanding & Processing:
Interpret the technical content of the Lessons Learned database.
Extract relevant insights while preserving technical accuracy.
Understand the root causes and background details provided for each item.
Data Analysis & Classification
Summarize, categorize, and classify all items based on:
- Topic
- Root cause
- Severity/importance
- Frequency
- Identify trends, patterns, and high-risk areas.
- Detect correlations and recurring issues.
Reporting & Documentation:
Deliver a detailed report summarizing key findings, including:
- Structured classification of Lessons Learned.
- Identified high-risk areas and trends.
- Correlation analysis and critical insights.
- Provide a clear explanation of all methodologies, tools, and processes used during the project.
Technical Requirements:
Specialized AI text mining tools should be used.
Chat-GPT, Deepseek, and similar general AI tools are not allowed.
The analysis should be conducted using technical and industry-specific text mining methodologies.
Ideal Candidate Skills & Experience
Proficiency in text mining techniques (e.g., NLP, machine learning for text analysis).
Experience handling large CSV/Excel datasets and extracting structured insights.
Strong analytical skills to understand and classify technical information.
Proven experience in similar projects—please provide examples of previous work.
I am seeking a skilled data analyst with expertise in text mining to extract and analyze specific information from a large list containing Lessons Learned records (approx 700 items). The database is in Excel format and consists of technical text entries that require a structured analysis.
Scope of Work:
The objective of this project is to conduct a technical analysis of Lessons Learned data, with a focus on understanding the key issues, identifying root causes, and categorizing insights. The analysis should include:
Data Understanding & Processing:
Interpret the technical content of the Lessons Learned database.
Extract relevant insights while preserving technical accuracy.
Understand the root causes and background details provided for each item.
Data Analysis & Classification
Summarize, categorize, and classify all items based on:
- Topic
- Root cause
- Severity/importance
- Frequency
- Identify trends, patterns, and high-risk areas.
- Detect correlations and recurring issues.
Reporting & Documentation:
Deliver a detailed report summarizing key findings, including:
- Structured classification of Lessons Learned.
- Identified high-risk areas and trends.
- Correlation analysis and critical insights.
- Provide a clear explanation of all methodologies, tools, and processes used during the project.
Technical Requirements:
Specialized AI text mining tools should be used.
Chat-GPT, Deepseek, and similar general AI tools are not allowed.
The analysis should be conducted using technical and industry-specific text mining methodologies.
Ideal Candidate Skills & Experience
Proficiency in text mining techniques (e.g., NLP, machine learning for text analysis).
Experience handling large CSV/Excel datasets and extracting structured insights.
Strong analytical skills to understand and classify technical information.
Proven experience in similar projects—please provide examples of previous work.