What Is Survey Sentiment Analysis?
Sentiment analysis is the process of using AI to determine the emotional tone of text — whether it's positive, negative, or neutral. Applied to survey responses, it goes beyond counting votes to understand how respondents feel about the topic.
Traditional survey analysis is quantitative: 60% chose Option A, 40% chose Option B. Sentiment analysis adds a qualitative layer: respondents who chose Option A expressed enthusiasm, while those who chose Option B expressed concern about pricing.
This richer understanding is what turns survey data into actionable intelligence.
How Untold Opinion's Sentiment Analysis Works
LLM-Powered Analysis
Untold Opinion uses a large language model to analyse your response data. The LLM reads all responses holistically — not just keyword matching — to understand the overall sentiment and key themes.
Sentiment Score (0–100)
Every poll gets an overall sentiment score from 0 (very negative) to 100 (very positive). The score updates live as new responses come in.
Trend Detection
The AI tracks how sentiment changes over time. If your product launch poll starts positive but sentiment drops after a pricing question, the trend analysis surfaces that pattern.
Key Insight Extraction
The AI generates specific, actionable insights: "72% prefer Feature A," "Top concern: pricing," "Negative sentiment concentrated in 35–44 age group."
Theme Clustering
For open-ended questions, the AI clusters responses by theme — surfacing the topics that matter most without manual reading of every response.
Have you ever used AI to analyse survey responses?
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Real Examples of Sentiment Insights
Product feedback survey
"Overall sentiment: 74/100 (Positive). Respondents are enthusiastic about the core product but express concern about the pricing model. Top theme: value for money."
Employee engagement survey
"Sentiment declined 18 points between Q2 and Q3. Key driver: responses about management communication became significantly more negative."
Event feedback survey
"92% positive sentiment overall. Top praise: speaker quality and networking opportunities. Top improvement area: venue logistics."
Brand awareness poll
"Neutral-to-positive sentiment (61/100). Brand recognition is high but emotional connection is weak — respondents know the brand but don't feel strongly about it."
Sentiment Analysis Accuracy
When to Use Sentiment Analysis
After a product launch — understand how customers feel, not just what they chose.
For employee surveys — detect morale trends before they become retention problems.
Post-event feedback — identify what worked and what to improve.
Brand research — measure emotional connection, not just awareness.
Customer satisfaction surveys — track sentiment over time to spot service quality trends.
Get AI sentiment analysis — free
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