Can BigSentiment analyze survey comments?
Yes. BigSentiment can analyze open-text survey comments when supplied or connected, then group them by sentiment and theme.
Voice of Customer Sentiment Analysis
Voice of customer sentiment analysis for reviews, surveys, support feedback, and public context. Find themes and report what customers feel.
Understand what customers are saying and how they feel. BigSentiment analyzes reviews, surveys, support feedback, and public context, then turns customer voice into clear reports.
What is voice of customer sentiment analysis?
Voice of customer sentiment analysis measures the emotional tone and recurring themes in customer feedback. It helps teams understand what customers praise, what frustrates them, and which issues are strong enough to affect retention, referrals, or reputation.
BigSentiment keeps direct customer voice separate from public context. A customer support complaint, a review, a Reddit discussion, and a news article can all matter, but they should not be interpreted as the same kind of signal.
Who uses voice of customer sentiment analysis
- CX leaders - Find recurring customer issues and positive experience drivers
- Product teams - Connect feedback themes to product and roadmap decisions
- Support leaders - Identify patterns in support comments and escalation reasons
- Executives - See customer sentiment summarized without reading raw comments
How BigSentiment analyzes customer voice
- Import or connect feedback - Use reviews, survey comments, support exports, app reviews, testimonials, and other supplied feedback.
- Score sentiment - AI classifies each comment by tone, urgency, theme, source, and confidence.
- Cluster themes - Recurring topics such as pricing, quality, support, wait time, trust, usability, or delivery are grouped.
- Add public context - When useful, public reviews, social discussion, news, and forums can be analyzed as a separate context layer.
- Report actions - Reports summarize the most important customer signals and recommended next steps.
Voice of customer data sources
Sources can include Google Reviews, Yelp, app store reviews, survey comments, NPS verbatims, support tickets, chat transcripts, testimonials, cancellation reasons, and uploaded feedback exports.
BigSentiment reports source counts and caveats so teams know whether a customer issue is isolated, recurring, or broadly supported.
Decisions VoC sentiment analysis supports
- Which customer issues deserve operational follow-up
- Which positive themes should be amplified in marketing
- Whether a product or service change improved sentiment
- Which feedback themes are becoming public reputation risks
- What leadership needs to know about customer experience health
Why BigSentiment fits VoC reporting
- Direct voice separation - Customer feedback is analyzed as its own layer
- Theme-level clarity - Reports show the topics behind the sentiment score
- Public context when needed - Customer feedback can be compared with social, review, news, and forum signals
- Executive-ready output - Findings are packaged for teams that need action, not just exploration
Frequently asked questions
How is this different from a survey platform?
Survey platforms collect structured feedback. BigSentiment focuses on interpreting sentiment across supplied customer feedback and public reputation channels.
Can customer sentiment be compared with public sentiment?
Yes. BigSentiment keeps customer voice and public context separate so teams can compare them without blending unlike signals.
Related BigSentiment pages
- Customer Sentiment Analysis - Use Case
- Review Sentiment Analysis - Use Case
- Qualtrics Alternative - Comparison