Understand how customers feel at scale. AI that analyses feedback, reviews, and conversations to reveal what people really think about your business.
Customer opinions are everywhere: in reviews, surveys, support conversations, social media, and emails. Reading all of it is impossible. Sentiment analysis uses AI to process this volume, revealing what customers think and feel about your business, products, and service.
AI can extract understanding from unstructured text:
Overall sentiment: Whether customers feel positive, negative, or neutral about your business.
Specific topics: What aspects drive satisfaction or frustration. Product quality, service speed, policies, or specific features.
Emotional intensity: Distinguishing mild disappointment from active anger, casual satisfaction from genuine enthusiasm.
Trends over time: How sentiment changes following product launches, policy changes, or events.
Comparative insight: How sentiment differs across products, locations, channels, or customer segments.
Sentiment analysis can process text from many sources:
Customer reviews: Product reviews, app store ratings, Trustpilot, and similar platforms.
Survey responses: Open-ended feedback from NPS, CSAT, and other surveys.
Support conversations: Chat transcripts, email threads, and call recordings.
Social media: Mentions, comments, and discussions about your brand.
Internal feedback: Employee surveys, suggestion boxes, and internal communications.
Each source offers different insight; combining them provides a complete picture.
Sentiment insight supports decisions across the organisation:
Product development: Understanding what customers love and hate about current offerings.
Service improvement: Identifying pain points in customer experience.
Brand monitoring: Tracking reputation and responding to emerging issues.
Competitive intelligence: Understanding how customers compare you to alternatives.
Crisis detection: Spotting problems early before they escalate.
Campaign effectiveness: Measuring response to marketing and communications.
Modern sentiment analysis uses language models that understand context:
Beyond keywords: AI interprets meaning rather than just counting positive and negative words.
Sarcasm and nuance: Understanding that "great, another delay" is not positive.
Aspect extraction: Identifying what specifically is being praised or criticised.
Multi-language support: Analysing feedback in different languages consistently.
Custom training: Adapting to your industry terminology and customer language.
We build sentiment analysis systems appropriate to your needs:
Source identification: Determining what text to analyse and how to access it.
Model selection: Choosing appropriate AI for your volume and requirements.
Integration: Connecting to your data sources and analytics platforms.
Customisation: Training models on your specific domain and vocabulary.
Reporting: Creating dashboards and alerts that surface useful insight.
Continuous improvement: Refining accuracy based on validated results.
Organisations using sentiment analysis typically gain:
Visibility: Understanding of customer opinion that was previously invisible.
Speed: Issues identified in hours rather than weeks.
Scale: Processing volumes that manual review could never handle.
Objectivity: Consistent analysis not subject to individual interpretation.
Actionability: Specific insight that drives concrete improvements.
Sentiment is most useful when it is tied to context.
Combine sentiment with topics. Knowing customers are unhappy is less useful than knowing what they are unhappy about.
Handle nuance and ambiguity. Sarcasm, mixed sentiment, and domain language can confuse generic models unless you evaluate and tune carefully.
Design for feedback loops. Insights only matter if someone owns the actions and you can see whether sentiment improves afterwards.
Use these prompts to design a sentiment system that produces insight you can trust.
“Which channels should we analyse first, and what questions do we want sentiment to answer for the business?”
“What topics should we extract alongside sentiment so we can identify root causes?”
“How should we validate accuracy: sampling, human review, and comparison across models?”
It can be, if you evaluate it on your real data and combine it with topic extraction and sampling. We avoid treating it as a perfect “truth machine”.
Better than keyword approaches, but it still needs evaluation and tuning for your domain and channels.
Usually no. Start with one or two high-signal sources (support transcripts, reviews), prove value, then expand.
Clear definitions, confidence scoring, human review samples, and drill-down to sources so users can verify conclusions.
Faster detection of issues, clearer prioritisation of fixes, and measurable improvement in sentiment over time.
