NLP: Classification, Summarisation and Sentiment
Turning paragraphs of feedback, reviews, field notes and survey responses into fields you can report on — categorised, sentiment-scored and summarised — for any written text your business collects but rarely reads in full.
The Job This Does
This applies to any free text your business collects — feedback forms, reviews, field notes, survey responses — not only a support inbox, which is covered on its own page. The output is structured signal: category, sentiment and summary, not another block of text to read.
What Gets Done to Your Text
Classification Into Defined Categories
Text sorted into categories you define, rather than a generic label set.
Sentiment Scoring
A sentiment score attached to each piece of text, consistent across large volumes.
Long-Text Summarisation
Long entries condensed into a short summary a person can scan quickly.
Key Theme and Pattern Extraction
Recurring themes surfaced across many entries at once, not visible when reading one at a time.
Where This Applies
- Customer feedback forms and product reviews.
- Site-engineer or field-staff notes.
- Open-ended survey responses.
- Social media mentions of your brand.
Building the Model
Category and Sentiment Scale Definition
Agreeing the categories and scale that matter to your business.
Labelled Sample Review
Reviewing a sample of your own text to confirm the categories fit.
Model Testing Against Your Own Text
Testing against your actual language, not a generic dataset.
Reporting Dashboard Setup
Results made visible in a dashboard your team can act on.
When the Volume Is Small Enough to Read
A small volume of text a person can read directly doesn't need this. It earns its place at a volume that currently goes unread because nobody has time for it.
Frequently asked questions
Does this work with Hindi or regional-language text?
Yes, classification and sentiment scoring can be built to handle Hindi and common regional-language or code-mixed text.
What drives the cost of building this?
The number of categories defined and the volume of text processed regularly.
What drives how long this takes to tune?
How much labelled or representative sample text is available to test the categories against.
Does this feed into our existing reporting?
Yes, results are built to feed into your existing dashboard, spreadsheet or BI tool rather than a separate reporting screen.
Who owns the categories and model?
You do — the category definitions, model and processed data stay yours.
What sample text do we need from you?
A sample of your actual text data and agreement on the categories or sentiment scale that matter to you.
Tell us what you need.
Send a short brief and one of our engineers will come back to you — usually the same day.
- No obligation
- We reply the same working day
- Your details stay private