Clean & Visualize Customer Reviews

I have a raw file of customer reviews that first needs proper text-based cleaning and then a set of clear, insightful visualizations. The main cleaning goal is straightforward: strip out anything irrelevant—think HTML remnants, signatures, marketing blurbs, duplicated lines, or other clutter that hides the real voice of the customer. Once the noise is gone, I’d like to see the data come alive through visuals that make trends obvious at a glance. Word clouds, sentiment distributions, and top-mentioned themes are the kinds of charts I have in mind, but I’m open to suggestions if you know more compelling ways to tell the story.

You’re welcome to work in Python with pandas, spaCy or NLTK for preprocessing, then use Matplotlib, Seaborn, Plotly, Tableau, or Power BI for the actual charts—whatever stack you’re most efficient with, as long as the output is shareable and easy for me to reuse later.

Deliverables
• A cleaned CSV (or comparable format) containing only relevant review text and any derived fields we agree on (e.g., sentiment score, key phrases).
• A small, well-commented script or notebook that reproduces the cleaning steps end-to-end.
• A collection of ready-to-present visualizations (PNG, PDF, or interactive dashboard) with a brief read-me explaining each chart.

Acceptance criteria
1. The cleaned dataset contains no obvious irrelevant fragments and shows consistent encoding.
2. The notebook/script runs without errors on my machine and produces identical output.
3. Visualizations accurately reflect the cleaned data and highlight at least sentiment, keyword frequency, and review counts.

If this fits your expertise, I’m ready to supply a sample of the reviews so you can confirm scope before we begin.