Your customers tell you what they think every day - in reviews, support tickets, social media posts, survey responses and call transcripts. The problem is volume. A business with thousands of customer interactions per week cannot read every one. Sentiment analysis uses natural language processing to read them all, categorise the emotion behind each one and surface the patterns that matter.

What sentiment analysis reveals

Product and service satisfaction - track how customers feel about specific products, features or service interactions over time. Identify which aspects generate positive sentiment and which consistently produce frustration, without relying solely on star ratings that flatten nuance into a single number.

Brand perception shifts - detect changes in how people talk about your brand before they show up in revenue numbers. A gradual increase in negative sentiment around delivery times or customer service can be identified and addressed weeks before it becomes a visible business problem.

Competitive intelligence - analyse public sentiment around competitor products and services to identify opportunities. If customers consistently complain about a competitor's pricing model or support quality, that is a market gap you can target.

Campaign and launch feedback - measure real-time reaction to product launches, marketing campaigns and policy changes. Rather than waiting for quarterly survey results, sentiment analysis provides immediate feedback on how your audience is responding.

How it works

Sentiment analysis is a specialisation within natural language processing (NLP) that identifies and categorises the emotional tone of text. Our models go beyond simple positive, negative and neutral classification.

Fine-grained analysis - we detect degrees of sentiment (strongly positive through to strongly negative) and identify mixed sentiment where a customer praises one aspect while criticising another within the same message.

Aspect-based extraction - rather than scoring an entire review as positive or negative, we extract sentiment about specific aspects. A hotel review might be positive about location, neutral about rooms and negative about food - aspect-based analysis captures all three.

Contextual understanding - modern transformer-based models handle sarcasm, irony, industry jargon and regional expressions far more accurately than earlier keyword-based approaches. We fine-tune models on your specific domain for higher accuracy.

Data sources we work with

We connect to the data sources where your customers express opinions: product reviews, app store ratings, social media platforms, customer support systems, survey tools, call transcription services and internal feedback channels. Data is ingested, processed and made available through dashboards built on Apache Superset or integrated into your existing analytics platforms.

Privacy and compliance

Sentiment analysis processes text data that may contain personal information. We configure all processing pipelines with data minimisation in mind - extracting sentiment signals without retaining or exposing personal identifiers. For organisations with strict data residency requirements, all processing runs on private infrastructure within the UK.