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Sentiment Analysis is the process of using AI to understand how users feel based on what they say or write — whether their tone is positive, negative, or neutral.
Why It Matters
- Helps you understand user satisfaction without waiting for surveys
- Flags frustrated or unhappy users early so you can act fast
- Improves product decisions by aligning features with emotional feedback
- Enhances customer support by prioritising negative sentiment
Use This Term When...
- You’re analysing app reviews, social media mentions, or support tickets
- You want to track emotional trends over time
- You’re building features for chatbots, support, or feedback automation
- You’re measuring how users feel about a new feature or update
- You’re trying to identify what’s causing churn or poor ratings
Real-World Example
In one of our projects, we used sentiment analysis to evaluate user feedback and app store reviews. This helped us identify recurring pain points and emotional triggers, guiding feature improvements and prioritising fixes that directly enhanced user satisfaction.
Founder Insight
Many founders collect feedback but never emotionally interpret it. Sentiment analysis turns words into actionable emotions — giving you the “why” behind the data.
Key Metrics / Concepts
- Polarity Score – A numeric value showing positive, neutral, or negative tone
- Emotion Classification – Tags like anger, joy, or disappointment from user input
- Volume by Sentiment – Total mentions by emotional category
- Sentiment Over Time – Trends in user emotions across product updates
Tools & Technologies
- MonkeyLearn / Lexalytics – Used for processing and categorising sentiment
- Google Cloud Natural Language – An AI service that detects sentiment in text
- IBM Watson Tone Analyzer – Identifies emotions and tones in written feedback
- Appbot – Connects to app reviews and customer feedback platforms
What’s Next / Future Trends
Sentiment analysis is becoming more contextual and multilingual, with AI learning to detect sarcasm, intent, and subtle tone shifts. It’s also moving into real-time environments like live chats, voice input, and social listening dashboards.
Related Terms
Voice Analytics – Similar analysis applied to spoken feedback
User Feedback – Source of sentiment-rich insights
AI – The underlying tech that powers sentiment models
Chatbots – Can be trained to react based on sentiment cues
UX Audit – Often supported by emotion tracking in user responses
Helpful Videos / Articles / Pages
Blog: How to Use Machine Learning in Web App Development
Blog: Do I need AI and Machine Learning For My Development Project
Call to Action
Interested in what your users are really feeling? Book a discovery call with us to explore how sentiment analysis can give your product an emotional edge.