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Qualitative Research

Sentiment Analysis

Sentiment Analysis

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Definition:

Sentiment analysis is a core technique in qualitative research that uses linguistic and computational methods to detect emotional tone across participant responses. By systematically coding language for positive, negative, or neutral sentiment, researchers can surface patterns that would be difficult to identify through manual review alone. In the context of qualitative research, sentiment analysis goes beyond simple keyword counting to capture nuance, context, and intensity of feeling. Enterprise research teams use it to prioritise themes, track shifts in customer perception, and translate large volumes of unstructured feedback into actionable insights that inform product, brand, and experience decisions.

How Conveo Does It

Conveo applies sentiment analysis automatically across AI-moderated video interviews, processing spoken and written responses from real participants at enterprise scale. Researchers can launch a study in under 30 minutes and receive sentiment-coded findings within days, not weeks. Because Conveo works exclusively with genuine human respondents rather than synthetic avatars or AI-generated answers, the sentiment signals reflect authentic emotional experience, giving enterprise teams the confidence to act on what they find.

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Frequently asked questions.

Sentiment analysis in qualitative research is the systematic process of evaluating participant language to determine emotional tone. It classifies responses as positive, negative, or neutral, and can also detect more granular emotions such as frustration, enthusiasm, or uncertainty. Researchers use it to move efficiently through large volumes of unstructured data and identify which themes carry the strongest emotional weight for participants.
Enterprise research teams often work with large datasets gathered across multiple markets, segments, or touchpoints. Sentiment analysis allows them to quickly identify where strong positive or negative feelings are concentrated, helping prioritise which issues need immediate attention. Without it, analysts risk missing critical emotional signals buried in hundreds of interview transcripts or open-ended responses. It turns qualitative volume into a strategic asset rather than an analytical burden.
Thematic analysis identifies recurring topics or patterns in qualitative data, focusing on what participants are talking about. Sentiment analysis focuses on how participants feel about those topics. The two approaches are complementary rather than competing. Thematic analysis tells you that customers frequently mention onboarding, while sentiment analysis tells you whether their feelings about onboarding are largely positive or negative. Combining both gives researchers a fuller picture of participant experience.
AI has significantly improved the accuracy and speed of sentiment analysis by enabling models to understand context, sarcasm, and linguistic nuance that simpler rule-based systems miss. Modern AI can process spoken responses from video interviews, not just written text, expanding the range of data that can be analysed. For enterprise teams, this means sentiment insights are available much faster and at a scale that manual coding could never match, without sacrificing analytical depth.
Enterprise teams typically apply sentiment analysis after fieldwork to score and segment participant responses before deeper analysis begins. Common applications include tracking brand perception across customer segments, evaluating emotional reactions to new product concepts, and identifying pain points in customer journeys. Teams often combine sentiment scores with demographic or behavioural data to understand which groups hold the strongest views, enabling more targeted and evidence-based decisions across product, marketing, and customer experience functions.

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