Sentiment Analysis

Review Sentiment Analysis: Understand What Customers Really Think

Go beyond star ratings. Use AI to analyze the emotions, topics, and trends in your reviews to understand customer experience and improve your business.

Updated: November 20249 min read

Key Takeaways

  • Sentiment analysis reveals the "why" behind your star rating
  • AI categorizes feedback by topic (service, price, quality, etc.)
  • Track sentiment trends over time to measure improvements
  • Identify issues before they become major problems
  • Discover what differentiates you from competitors

What is Sentiment Analysis?

Sentiment analysis uses artificial intelligence to examine the text of customer reviews and determine the emotional tone—positive, negative, or neutral. But modern AI goes much deeper than simple classification.

Our sentiment analysis examines:

  • Emotional intensity: Not just positive or negative, but how strongly customers feel
  • Topic extraction: What specific aspects of your business are customers discussing?
  • Aspect sentiment: Different sentiment for different parts of the experience (great food, poor service)
  • Comparative sentiment: How you compare to mentions of competitors
  • Trend detection: How sentiment changes over time

While a star rating tells you the "what," sentiment analysis tells you the "why."

How AI Sentiment Analysis Works

Our AI uses advanced natural language processing (NLP) to understand reviews:

Text Processing

First, the AI processes the raw review text, handling slang, abbreviations, emojis, and contextual clues. It understands that "not bad" is actually positive and "could have been better" is negative.

Entity Recognition

The AI identifies specific entities mentioned in reviews—staff members by name, specific products, services, or aspects of your business like "parking" or "wait time."

Sentiment Scoring

Each review and each aspect within a review gets a sentiment score from -1 (extremely negative) to +1 (extremely positive), with 0 being neutral.

Categorization

Reviews are automatically categorized by topic: service quality, pricing, product quality, ambiance, cleanliness, location, and other industry-specific categories.

Pattern Detection

The AI identifies patterns across all your reviews—recurring praise, common complaints, and emerging issues that may need attention.

Key Metrics & Insights from Sentiment Analysis

Our dashboard provides actionable insights from your review sentiment:

Overall Sentiment Score

A composite score that represents the overall emotional tone of your reviews. Unlike star ratings, this accounts for the actual content of what people write.

Sentiment by Category

See how customers feel about specific aspects of your business:

  • Service & Staff
  • Product/Food Quality
  • Pricing & Value
  • Cleanliness & Ambiance
  • Wait Time & Convenience
  • Location & Accessibility

Keyword Sentiment Map

Visual representation of which words and phrases appear most often in positive vs. negative reviews.

Staff Mentions

Track which employees are mentioned positively (for recognition) and who might need additional training or support.

Competitor Comparisons

When reviews mention competitors, AI analyzes the context to understand your competitive positioning.

Taking Action on Sentiment Insights

Sentiment analysis is only valuable if you act on it. Here's how to turn insights into improvements:

Address Recurring Negatives

If multiple reviews express negative sentiment about the same topic, that's a clear area for improvement. Create an action plan to address the root cause.

Double Down on Positives

Identify what customers consistently love and emphasize it. Use positive sentiment topics in your marketing and train staff to deliver more of what works.

Set Alerts

Configure alerts for when sentiment drops below a threshold or when specific negative topics appear. Catch issues early before they become patterns.

Staff Recognition

Use positive staff mentions to recognize and reward employees. This motivates the team and reinforces desired behaviors.

Respond Strategically

Use sentiment insights to craft better review responses. Address the specific emotional tone and concerns in each review.

Single snapshots are useful, but trend analysis reveals the full picture:

Before/After Analysis

Made changes to your business? Track how sentiment shifts after implementing improvements. This validates what's working.

Seasonal Patterns

Some businesses see sentiment fluctuations by season. Understanding these patterns helps you prepare and set appropriate expectations.

Impact Measurement

Measure the impact of training programs, new hires, menu changes, or other initiatives through sentiment changes.

Early Warning System

Declining sentiment trends can signal problems before they significantly impact your overall rating. Address issues proactively.

Competitive Tracking

Monitor how your sentiment compares to competitors over time. Identify when you're gaining or losing ground.

Frequently Asked Questions

What's the difference between star rating and sentiment?

Star rating is a simple number, but sentiment analysis examines the actual text to understand why customers feel the way they do. A 3-star review might have positive sentiment about food but negative about service.

How accurate is AI sentiment analysis?

Our AI achieves over 90% accuracy on sentiment classification. It understands context, sarcasm, and nuance better than basic keyword analysis.

Can sentiment analysis work in multiple languages?

Yes. Our AI supports sentiment analysis in 25+ languages, automatically detecting the language and analyzing sentiment appropriately.

How do I use sentiment insights to improve my business?

Focus on recurring negative themes and address root causes. If multiple reviews mention slow service, that's actionable feedback. Use positive sentiment topics in your marketing.