Sentiment Analysis
AI-powered analysis of text to determine whether feedback is positive, negative, or neutral.
Category
Analytics & Insights
Full Definition
Sentiment analysis uses artificial intelligence and natural language processing (NLP) to automatically determine the emotional tone of text-based feedback. It classifies customer comments as positive, negative, or neutral, and can detect specific emotions.
How Sentiment Analysis Works: 1. Text Processing: Feedback is cleaned and prepared 2. Feature Extraction: Key words, phrases, and patterns are identified 3. Classification: AI models determine sentiment polarity 4. Scoring: Feedback receives a sentiment score (e.g., -1 to +1)
Types of Sentiment Analysis: - Polarity Detection: Positive, negative, or neutral - Emotion Detection: Joy, anger, sadness, fear, surprise - Aspect-Based: Sentiment about specific topics (service, price, quality) - Urgency Detection: Identifying time-sensitive issues
Common Use Cases
Real-World Examples
Scenario
A hotel receives 500 reviews. AI sentiment analysis processes them in seconds: 320 positive, 95 neutral, 85 negative. Negative reviews are auto-flagged.
Outcome
Manager focuses on the 85 negative reviews first. Discovers most mention "parking fees" β a new policy causing backlash.
Scenario
Customer writes: "The delivery was fast but the packaging was a disaster. Product arrived broken. Really disappointed." Sentiment: NEGATIVE. Emotion: Frustration. Aspect: Packaging.
Outcome
E-commerce company tags this for their packaging team (not delivery team) to address. Specific routing enables faster resolution.
Scenario
AI detects that sentiment around "pricing" shifted from 60% positive to 30% positive after a price increase announcement.
Outcome
Company adjusts their communication strategy, adds more value messaging, and offers loyalty discounts to long-term customers.
Related Terms
Text Analytics
The process of extracting meaningful insights from unstructured text feedback.
Verbatim Feedback
Word-for-word customer comments captured through open-ended survey questions.
Theme Analysis
Categorizing customer feedback into recurring topics and patterns.
AI-Powered Analysis
Using artificial intelligence to automatically analyze and extract insights from customer feedback.