NLP
NLP Pipeline for Quantitative Text Intelligence
Pipeline to transform unstructured text into quantitative indicators, recurring themes and executive summaries.
Dataset type: public. No confidential client or employer data.
Executive Summary
This case outlines an NLP pipeline that turns unstructured text into measurable indicators, recurring themes and concise summaries for decision review.
Business Question
What themes, entities and sentiment patterns appear in large volumes of text, and how can they be summarized without losing traceability?
Statistical Question / Hypothesis
The analysis evaluates whether extracted topics, entities and sentiment indicators are stable enough to support monitoring and prioritization.
Dataset
The dataset is public and contains unstructured text records with metadata such as date, source and category. No confidential text is used.
Methodology
The workflow applies cleaning, tokenization, sentiment analysis, topic modeling, entity extraction, summarization and text classification. Human review is included for labels and interpretation.
Implementation
Python is used to process text, generate structured indicators and evaluate classification outputs. LLM-assisted summarization is constrained by traceable source snippets and documented limitations.
Results
Results include topic clusters, entity frequency tables, sentiment distributions and executive summaries linked to source evidence.
Limitations
Limitations include label ambiguity, language variation, model bias, hallucination risk in summaries and the need for human validation.
Executive Recommendation
Use the pipeline for recurring text monitoring and prioritization, with human review for decisions that require nuance or high confidence.
Tools Used
Python, NLP tooling and LLMs.
Links
Notebook, GitHub repository and executive PDF are coming soon.