All projects · Machine learning

Drug feedback sentiment and recommendation analysis

I used my MSc dissertation project to explore how messy patient review data can be turned into sentiment and recommendation-style reporting, with clear warnings around limitations.

I used Python, NLP and transformer model experimentation on patient review text, then created a Power BI reporting layer for sentiment, rating, side-effect and model-output exploration.

Public review data

Dataset type: Public review data. This label is shown clearly so the project is honest about whether the evidence is public, mock, synthetic or portfolio data.I separate source type from method because the reliability and limitations depend on where the data came from.

Drug feedback sentiment and recommendation analysis main dashboard preview

Why this matters

Build summary

Patient-review text is messy, inconsistent and subjective. This project explores how NLP can summarise patterns while being careful about limitations.

I used Python, NLP and transformer model experimentation on patient review text, then created a Power BI reporting layer for sentiment, rating, side-effect and model-output exploration.

Key numbers

534
drugs
4,013
comments
6.94
avg rating

What I wanted the report to help with

How I approached it

  • Make the main performance question easier to scan.
  • Separate headline totals from the detail that explains them.
  • Show enough context for a non-technical user to trust the next action.
  • Cleaned review text and rating fields.
  • Tested sentiment and transformer-based approaches.
  • Built reporting views for sentiment, ratings and side-effect themes.

Technical inspection notes

  • Treated patient review text as messy qualitative data, not a clean rating table.
  • Used NLP and transformer experimentation to explore sentiment and recommendation-style signals.
  • Kept Power BI as the reporting layer, not the modelling layer.
  • Made limitations visible because drug feedback is not clinical advice and model output needs careful interpretation.

Dashboard snapshots

These snapshots show how the analysis was turned into decision-ready visuals.

Click any screenshot to inspect the dashboard evidence more closely.

Drug feedback sentiment and recommendation analysis snapshot
Sentiment dashboard: Shows review volume, rating and sentiment measures.
Drug feedback sentiment and recommendation analysis snapshot
Drug comparison: Helps compare drugs by rating and review signals.
Drug feedback sentiment and recommendation analysis snapshot
Report view: Shows the main dashboard states together.

What the analysis showed

  • The dashboard view included 534 drugs.
  • It included about 4,013 comments.
  • Average rating was 6.94 in the selected view.

What I would improve next

  • Add stronger model validation.
  • Avoid clinical recommendation claims without expert review.
  • Add confidence and bias notes beside model outputs.