Synopsys / NATURAL LANGUAGE PROCESSINGProduction experience

NLP for Support: Turning Ticket Text into Sentiment Signals

An NLP classification system that turns support-ticket text into sentiment signals.

PythonNLPSentiment classification

SYSTEM OVERVIEW

the pieces, connected.

Support tickets (Text feedback) → Text processing (NLP workflow) → Sentiment classification (NLP classifier) → Service-quality insights (Analyze the support experience) — A public sketch of the documented components and workflow. Internal interfaces are omitted.
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THE PROBLEM

Where the work started.

Large collections of support tickets make it difficult to assess service experience consistently. Sentiment classification provides another way to analyze that feedback.

MY CONTRIBUTION

What I built.

Developed a sentiment-classification system for support tickets.
Applied NLP to turn ticket text into signals for service-quality analysis.
Worked with experiment tracking and distributed ML infrastructure in the broader engineering role.

THE RESULT

100K+support tickets analyzed

Turned support-ticket text into sentiment signals for service-quality analysis.

Selected work from my engineering role at Synopsys. Impact figures are reported from this work; the companion examples below use synthetic data.

CODE & EVALUATION

Inspect the work behind the explanation.

SYNTHETIC COMPANION EXAMPLE

Classification accuracy and coverage for sentiment labels

A new, runnable example that scores saved predictions against expected outputs. It uses fictional data and illustrates evaluation mechanics; it does not reproduce the production system or substantiate the impact figures above.

python3 evaluate_outputs.py sentiment.json report.json

Download runnable examples Read the script Sample report

Python 3.10+ · Standard library only · Includes fixtures, metric definitions and tests

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