Sentiment AnalysisSolutions
Understand how customers feel, and about what, across thousands of messages that nobody has time to read one by one.
Sentiment Analysis Solutions: what the work involves
Feedback arrives from everywhere: survey comments, support chats, app store reviews, social replies, WhatsApp messages. Managers sample a handful, remember the angriest, and miss the slow drift in how people talk about delivery, pricing or a new feature. Star ratings alone hide the reason, and a five-star review can still contain a complaint.
We score text at two levels: overall feeling and sentiment per topic, so a message that praises the product but criticises the courier is recorded as both. Depending on volume and language we choose between a prompted language model, a fine-tuned multilingual encoder, or a combination, always judged against a sample of your own messages labelled by your staff. Sarcasm, negation and Roman Urdu spelling are tested explicitly. Each score carries a confidence value, and a dashboard shows trends by topic, channel and week, with the original messages one click away. Strongly negative items can alert a manager in chat.
Core features
Overall and per-topic sentiment
One message can be positive on quality and negative on shipping, and both are recorded separately.
Multi-language handling
English, Urdu script and Roman Urdu are processed, with spelling variants normalised before scoring.
Topic discovery
Recurring themes are grouped automatically, so you see what customers talk about, not just whether they are happy.
Trend dashboard
Charts show movement by topic, channel, branch or product over time, linked to the underlying messages.
Alerts on strong negatives
Very negative or urgent messages notify a manager in Slack or email with the text and customer record.
Labelled test set
Quality is measured against messages your own staff labelled, and re-checked as language and products change.
What we get right before launch
Sarcasm and mixed feelings
Models misread irony and polite complaints, particularly in Roman Urdu. We test those cases explicitly, show confidence, and send ambiguous items to a person.
Averages that hide problems
A single score across all feedback can mask a failing branch or product. Reporting is split by segment, and small groups are marked as too few to draw conclusions.
Using sentiment on individuals
Scoring a person's mood to decide how to treat them raises fairness issues. We use it for aggregate insight and prioritisation, not to penalise customers or staff.
Tools and technology
- Python
- OpenAI
- Hugging Face Transformers
- scikit-learn
- FastAPI
- PostgreSQL
- Metabase
- n8n
Common questions, answered
How accurate is sentiment analysis?
It varies with language, topic and how ambiguous your messages are. Instead of promising a figure, we test on a labelled sample of your own text and report results per category so you can judge it.
Can it understand Roman Urdu?
Reasonably, with caveats. Inconsistent spelling and slang cause errors, so we normalise text, add examples from your data, and send low-confidence messages to human review.
Where does the feedback come from?
Surveys, helpdesk tickets, review sites, Google Business Profile, social comments and chat exports. Each source needs its own connector, and some platforms restrict automated collection.
Do we need a custom model?
Not always. A prompted language model often does well on small volumes. For very large volumes or strict privacy, a smaller fine-tuned model hosted by you may be cheaper and more controllable.
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Customer Churn Prediction
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Ready to start your Sentiment Analysis Solutions project?
Tell us what you need and we will come back with a clear scope, timeline and the questions worth answering before any build starts.