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NLP & AIPrototype

Cross-Platform Sentiment Intelligence Pipeline: Turning Messy Text into Structured Signals

An NLP pipeline for converting multi-platform text into sentiment, topic, and monitoring-ready signals.

NLP/data pipeline builder · 2024

  • Python
  • NLP
  • Transformers
  • ETL
Role
NLP/data pipeline builder
Status
Prototype
Year
2024
Type
NLP & AI
Access
Sanitized demo recommended

Overview

This pipeline converts multi-platform text into structured sentiment, topic, and monitoring-ready signals. It's built as a pipeline so messy, inconsistent text becomes something teams can actually track over time.

Problem

Useful signal is buried in high-volume, noisy text spread across very different platforms and formats. Reading it by hand doesn't scale, and one-off scripts don't produce anything you can monitor. The aim was a repeatable path from raw text to structured signals.

Pipeline workflow

  1. Source text — public or sanitized multi-platform content
  2. Ingestion into a common store
  3. Cleaning and normalization into a shared shape
  4. Sentiment and topic processing
  5. Structured output
  6. Monitoring via a dashboard or API

Text processing and modelling

Normalisation comes before any modelling, so downstream steps see a consistent shape regardless of source.

  • Tokenisation and text cleaning
  • Sentiment classification
  • Topic extraction
  • Transformer-based models where they earn their keep

Data outputs

Results are written to a structured form rather than printed once, so the signal can be tracked.

  • Per-item sentiment and topic labels
  • Aggregations ready for monitoring
  • Outputs suitable for a dashboard or API

Technical decisions

  • Normalise before modelling, so sources stay comparable
  • Write structured outputs, not one-off reports
  • Keep ingestion and processing as separate stages
  • Work from public or sanitized text, not private platform data

Limitations

  • Platform API and rate limits constrain ingestion
  • Slang and local context affect sentiment accuracy
  • Models can misread sarcasm and irony
  • Public text is noisy and can be biased
  • Monitoring outputs still need human interpretation

What it demonstrates

  • Designing a multi-stage data/NLP pipeline
  • Normalising messy text into structured signals
  • Reasoning about sentiment, topics, and monitoring
  • Building outputs that support ongoing tracking

Stack

  • Python
  • NLP
  • Transformers
  • ETL

Proof assets

Some proof assets use dummy data or are shared as private walkthroughs to protect sensitive systems and records.

  • DiagramPlanned

    Architecture diagram

    Ingestion, processing, and output stages.

    Planned — to be added

  • ScreenshotsPlanned

    Sample dashboard with dummy data

    A monitoring view on non-real data.

    Planned — to be added

  • DocumentationPlanned

    Sanitized notebook/API output

    Example structured outputs.

    Planned — to be added

  • GitHubComing soon

    GitHub

    Source repository.

    Coming soon

Availability

Sanitized demo recommendedAny demo runs on dummy data — no real or sensitive data is exposed.

Next steps

  • Add evaluation on a labelled sample
  • Support additional sources within their terms of use
  • Add a sanitized monitoring dashboard
  • Document the schema of the structured outputs