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Qualitative Research

Sentiment Score

Sentiment Score

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Definition:

A sentiment score is a quantified measure of emotional polarity derived from qualitative data, most commonly text or spoken responses gathered during research interviews, surveys, or focus groups. In qualitative research, sentiment scores help analysts move beyond manual interpretation by assigning a consistent, comparable value to participant language, whether that language expresses satisfaction, frustration, enthusiasm, or concern. Researchers use sentiment scores to identify patterns across large volumes of responses, prioritise themes for deeper analysis, and communicate findings to stakeholders in a format that is easy to act on. When combined with thematic coding and verbatim quotes, sentiment scoring adds a measurable layer to qualitative insight without reducing its richness.

How Conveo Does It

Conveo automatically generates sentiment scores across all responses collected through its AI-moderated video interviews, giving enterprise research teams a fast and consistent read on participant emotion at scale. Because Conveo works with real participants rather than synthetic respondents or AI avatars, the sentiment data reflects genuine human attitudes. Teams can launch a study in under 30 minutes and receive scored, analysed results within days, making it practical to track sentiment shifts across segments, geographies, or product iterations without lengthy manual analysis.

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Related terms.

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Frequently asked questions.

A sentiment score is a numerical indicator that captures the emotional tone of a participant response, rating it on a scale that typically runs from negative through neutral to positive. In qualitative research, it gives teams a way to quantify language that would otherwise require manual interpretation. Sentiment scores are most useful when applied across many responses, allowing researchers to spot emotional patterns and prioritise which themes deserve closer attention.
Enterprise research teams often collect hundreds or thousands of open-ended responses across studies, making manual emotional analysis impractical. Sentiment scoring provides a consistent, scalable way to surface how participants feel about a product, brand, or experience without reading every response individually. It also makes qualitative findings easier to present to stakeholders who expect data-driven evidence. When sentiment trends are tracked over time, they can signal shifts in customer attitude before those shifts show up in quantitative metrics.
A sentiment score measures emotional tone, telling you whether a response is positive, negative, or neutral. Thematic coding identifies the subject matter or topic being discussed, such as pricing, usability, or customer service. The two approaches are complementary rather than competing. Sentiment scoring tells you how participants feel, while thematic coding tells you what they are talking about. Combining both gives researchers a fuller picture, for example, identifying that participants feel negatively about a specific feature rather than the product overall.
AI has made sentiment scoring significantly faster and more nuanced. Earlier rule-based systems struggled with sarcasm, context, and domain-specific language, often producing unreliable scores. Modern AI models can interpret tone within context, account for hedging language, and process spoken responses from video interviews as well as written text. For research teams, this means sentiment scoring is no longer a post-processing task that takes days. It can be applied automatically as data is collected, enabling near real-time insight during a live research programme.
Enterprise teams typically use sentiment scores as a first-pass filter across large response sets, identifying which participant segments or question areas carry the strongest emotional signal. From there, analysts drill into the verbatim responses behind low or high scores to understand the reasoning. Sentiment scores are also used to benchmark studies over time, tracking whether attitudes toward a product or brand are improving or declining. In stakeholder reporting, they provide a concise, credible summary of participant emotion that complements qualitative quotes.

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