Sentiment Analysis
What is sentiment analysis?
Sentiment analysis is the use of AI (typically an LLM) to analyze text and determine the emotional tone, attitude, or sentiment expressed—such as whether the content is positive, negative, neutral, or in specific applications, indicators of risk or concern. It enables automated understanding of human emotional expression in written communication.
How is sentiment analysis used in product development?
Sentiment analysis can be applied across various product contexts:
Mental health and wellbeing — Analyzing student or employee responses to assess risk levels, identifying when someone might need additional support based on the emotional tone of their writing.
Customer feedback — Understanding customer sentiment in support tickets, reviews, or survey responses to prioritize issues and measure satisfaction.
Interview analysis — Detecting emotional sentiment in customer interview transcripts to identify frustrations, delights, or concerns that might not be explicitly stated.
The power of LLM-based sentiment analysis is that it goes beyond simple keyword detection. It can understand emotional context and nuance in how people express themselves.
Why use sentiment analysis in prototyping?
Sentiment analysis creates functional behavior that static design tools cannot simulate. When building prototypes, teams can connect to a real LLM for sentiment analysis, enabling stakeholders to experience how the product would actually work with real user responses.
This is particularly valuable for assumption testing during discovery work. Instead of describing how the product "would" analyze sentiment, teams can build working prototypes that actually analyze sentiment, giving stakeholders and users something concrete to react to and provide feedback on.
Learn more:
- AI Prototyping: How 11 Real-World Teams Are Transforming Their Work with Lovable
- Customer Interview Analysis: Where AI Helps and Hurts
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