Multilingual customer sentiment engine
NUT Botanicals · Consumer Feedback / Marketing
Tracked over the deployment window versus the prior baseline.
- Client
- NUT Botanicals
- Industry
- Consumer Feedback / Marketing
- Scope
- Multilingual customer-sentiment platform across channels.
Representative interface (sample data)
Challenge
NUT struggled to make sense of scattered customer feedback across platforms. The goal was to understand sentiment and drive product strategy.
Solution
A multilingual sentiment-classification engine that aggregates feedback from social media, e-commerce platforms, and surveys, then classifies it with machine-learning models (Naive Bayes, SVM, BERT) to support strategic decisions.
Our approach
We consolidate feedback from social media, e-commerce and surveys, then classify sentiment and topics across Arabic and English — Naive Bayes and SVM give explainable baselines while BERT captures richer context, sarcasm and product terminology. It runs an operating loop — ingest, anonymize, classify, aggregate, alert, assign, resolve and track each topic over time — surfacing topic trends, complaint categories, product-issue heatmaps and urgent-feedback queues, not just a positive/negative score. Language-quality review, bias testing, anonymization and escalation handle ambiguous or high-impact feedback, so emerging issues are caught early.