Strategy
Sentiment Analysis
A practical method for understanding perception, emotional tone, and recurring themes across large volumes of feedback.
How to use sentiment analysis to turn large volumes of user feedback into clearer signals around perception, emotion, and priorities.
Quick take
If you want to understand how users feel at scale, not just what they do, use sentiment analysis.
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What it is
Sentiment analysis is a UX and product method used to analyse user glossaryFeedbackFeedback is the system response that informs users about the result of their actions. It helps users understand what has happened and what to do next.Open glossary term and classify it as positive, negative, or neutral.
It is typically applied to large volumes of glossaryQualitative DataQualitative data is non-numerical information that describes user experiences, behaviours, and opinions.Open glossary term such as survey glossaryResponseA response is the data or result returned by a server after receiving a request.Open glossary term, reviews, support tickets, social media, and glossaryFeedbackFeedback is the system response that informs users about the result of their actions. It helps users understand what has happened and what to do next.Open glossary term forms.
This can be done manually or using natural language processing tools to glossaryProcessA process is a defined sequence of steps used to achieve a specific outcome.Open glossary term glossaryDataData is raw information collected and stored for analysis, processing, or decision-making.Open glossary term at scale.
Unlike metrics such as CSAT or NPS, which provide a score, sentiment analysis focuses on the language users use and the emotions behind it.
The goal is to understand overall perception, identify trends, and uncover issues that may not be visible through glossaryQuantitative DataQuantitative data is numerical information used to measure behaviours and performance.Open glossary term alone.
Sentiment analysis is useful when the volume of feedback is too large to read one by one, but the emotional signal still matters.
When to use it
Use this method when you need to understand user perception at scale.
It is most useful when:
It is less useful when:
Sentiment analysis is often used alongside surveys and user interviews to combine scale with depth.
Key takeaway
Use sentiment analysis when you need to understand patterns in perception and emotional tone across large datasets.
How to run it
Set up properly.
Before you start, be clear on what glossaryData SourceA data source is the origin from which data is collected or accessed.Open glossary term you will analyse, how sentiment will be classified, and whether analysis will be manual or automated.
Ensure glossaryDataData is raw information collected and stored for analysis, processing, or decision-making.Open glossary term is clean and relevant.
Run the method.
Sentiment analysis is glossaryPatternA reusable solution to a common design problem.Open glossary term-based and scalable.
Collect qualitative glossaryFeedbackFeedback is the system response that informs users about the result of their actions. It helps users understand what has happened and what to do next.Open glossary term from relevant sources. Categorise sentiment as positive, negative, or neutral. Identify common themes within each category. Use tools where needed to glossaryProcessA process is a defined sequence of steps used to achieve a specific outcome.Open glossary term large volumes. Segment glossaryDataData is raw information collected and stored for analysis, processing, or decision-making.Open glossary term where relevant, such as feature or journey.
Focus on glossaryPatternA reusable solution to a common design problem.Open glossary term across large glossaryDataData is raw information collected and stored for analysis, processing, or decision-making.Open glossary term sets.
Capture and make sense of it.
The value comes from identifying trends and themes.
Look across glossaryDataData is raw information collected and stored for analysis, processing, or decision-making.Open glossary term to identify overall sentiment distribution, recurring positive or negative themes, changes in sentiment over time, and differences between user groups or glossaryFeatureA feature is a specific piece of functionality within a product that delivers value to users. It represents something users can do or experience as part of the overall product.Open glossary term.
Use this to inform glossaryPrioritisationPrioritisation is the process of ranking tasks, features, or initiatives based on their importance, impact, and effort.Open glossary term and decision-making.
What to look for
Focus on:
Where it goes wrong
Most issues come from:
Not all glossaryFeedbackFeedback is the system response that informs users about the result of their actions. It helps users understand what has happened and what to do next.Open glossary term fits neatly into positive or negative.
What you get from it
Done properly, this method gives you:
Key takeaway
It helps you understand how users feel, not just what they do.
Get in touch
If this sounds like something you need, we can help you turn raw glossaryFeedbackFeedback is the system response that informs users about the result of their actions. It helps users understand what has happened and what to do next.Open glossary term into clear glossaryInsightAn insight is a meaningful understanding that explains why something is happening and what it means.Open glossary term and action.
No guesswork. No assumptions. Just understanding you can act on.
FAQ
Common questions
A few practical answers to the questions that usually come up around this method.
What is sentiment analysis in UX?
Sentiment analysis is a method used to classify and understand user glossaryFeedbackFeedback is the system response that informs users about the result of their actions. It helps users understand what has happened and what to do next.Open glossary term based on emotion and tone.
When should you use sentiment analysis?
Use it when analysing large volumes of qualitative glossaryFeedbackFeedback is the system response that informs users about the result of their actions. It helps users understand what has happened and what to do next.Open glossary term or monitoring perception over time.
How is sentiment analysis performed?
It can be done manually or using automated tools with natural language processing.
Is sentiment analysis accurate?
It can be effective at scale, but may miss nuance and should be combined with other methods.
Does sentiment analysis improve UX?
Yes. It helps identify emotional drivers and prioritise improvements.