Customer Sentiment Classification Engine
A production-ready NLP system that transforms unstructured customer feedback into categorised sentiment signals, enabling a wellness centre to identify service strengths and weaknesses at scale.
The Challenge
Wellness centres accumulate large volumes of customer feedback through reviews, surveys, and informal comments. This feedback contains valuable signals about what clients value and where service quality is inconsistent.
Without systematic analysis, these signals remain locked inside unstructured text. Feedback is read sporadically, patterns are missed, and service improvement decisions are based on anecdote rather than data.
Approach
Results
Classified 27,586 customer reviews with 92% accuracy using a fine-tuned BERT model, transforming an unmanageable volume of unstructured feedback into structured, categorised intelligence.
Beyond binary positive/negative classification, the thematic extraction layer identified specific recurring service issues - the kind of patterns that are invisible when feedback is read sporadically but become clear and actionable when analysed systematically.
The system gave the wellness centre something it did not have before: an evidence base for prioritising service improvements, replacing anecdotal impressions with data showing exactly which issues appeared most frequently and with the strongest negative sentiment.