Google reviews have increasingly become a vital tool for building a successful location-based business. Why? Google reviews dominate the review space, representing the vast majority of feedback across most industries. Properly analyzing these reviews can reveal important insights into what customers appreciate and what they find lacking, allowing you to make changes and improve your ratings. Additionally, well-crafted and informative responses to reviews can appeal to potential new customers and boost your ranking on Google Maps, attracting even more customers.
Together, the number of reviews, your ratings, the quality of your responses, and your position on the map rankings determine whether customers choose your business over your competition.
Advertising agencies, consulting firms, and reputation researchers now use AI tools to analyze tens or hundreds of thousands of reviews in ways that weren’t possible before the advent of AI. Analyzing reviews, or performing customer review sentiment analysis, specifically on Google reviews, can turn enormous quantities of unstructured data into actionable insights.
What is Sentiment Analysis for Google Reviews?
Sentiment analysis for Google reviews involves identifying and categorizing opinions and sentiments within textual reviews to determine customers' attitudes toward specific topics, products, and aspects of a business. If this blog is TL;DR, you can watch our Google Review Sentiment Analysis YouTube video instead!
Most review platforms and reputation management tools provide a surface-level analysis of your business locations. Metrics like total reviews, average ratings, and review response rates are important, but alone, they don’t provide in-depth insights into how your business performs across various topics and categories specific to your industry.
Advanced Customer Review Sentiment Analysis
Sentiment Analysis has been around for many years, but the advent of language models has changed the game. Learn more about the evolution of sentiment analysis and the use of keywords in our article about Advanced Customer Review Sentiment Analysis.
Google Review Keyword Analysis vs. Sentiment Analysis
Google's keyword-based filtering feature on Google Maps helps break down reviews by common keywords customers mention. However, this analysis doesn’t associate a positive or negative attitude with these keyword mentions. Many reputation management platforms also provide this high-level keyword-based analysis, allowing users to quickly see how many reviews mention certain keywords.
However, this doesn’t provide actionable data to move your business and customer experience forward. You need to read the review, find the section that mentions that keyword, determine the sentiment and topic it is part of, note that down, and move to the next review. Then, consolidate your notes and make sense of the analysis!
How RightResponse AI Performs Sentiment Analysis On Reviews
RightResponse AI goes deeper than other platforms, allowing you to understand your business locations more thoroughly through the lens of customer reviews. It uses the latest LLMs powered by generative AI and machine learning to identify customer sentiments in each phrase of your textual reviews, assign a topic/category, and label them as positive or negative.
This method of AI-powered sentiment analysis is superior in a number of ways, assisting you in understanding and improving your business:
Completely Customizable Categories and Topics—When you sign up with RightResponse AI and add your business location(s), our AI engine automatically provides draft Categories and Topics based on your industry. Each of those Categories and Topics is completely customizable, so you can visualize your reviews in a way that aligns closely with how your business operates.
Percent Positive Mentions - Text reviews have become longer in recent years. With more data associated with customer reviews, star ratings have become less helpful. Even in highly rated reviews, customers sometimes note things they found lacking. Our “% positive mentions” metric lets you quickly drill down into how satisfied your customers are about each specific Topic. For example, in a restaurant setting, for example, you could see that customers are very happy about your staff’s knowledge but not so happy about the quality of your ingredients.
Cross-Compare Locations, Topics, & Categories—Analyzing unstructured data by topic and category allows you to gain actionable insights across several business locations. This enables you and your operations managers to drive change that fosters customer satisfaction and business growth more effectively using data that typically isn’t analyzed in-depth.
For instance, in this real example we have multiple restaurant locations and are analyzing their customer reviews with RightResponse AI. Note that the red dot restaurant has an average rating of 4.1. In the second chart, the green dot restaurant has an average rating of 4.2:
- The red dot restaurant location had the best % positive mentions in Food, but the worst in Service! This location’s average rating was 4.1
- The green dot restaurant location reported the worst positive mentions in Food, yet the second best in Service. And this location’s average rating was 4.2
These insights quickly helped this team realize that, even though the overall ratings were similar, they needed to do different things in each location – improve service at the red dot location and improve food preparation at the green dot location!
How can you do sentiment analysis on Google reviews?
To efficiently perform sentiment analysis on your own Google reviews, sign up for a free trial (no credit card required) of RightResponse AI and connect your Google Business location. As your reviews roll in, they will be analyzed for sentiment and displayed on your dashboard by our AI-powered review sentiment analysis software. Once you start your paid subscription, you have the option to analyze your historical reviews as well.
After analyzing your business’s reviews for sentiment, the interactive RRAI dashboard visualizes these sentiments across topics and categories. Quickly analyze those topics & categories by % positive mentions, determine which aspects need improvement, and compare that data across multiple locations to prioritize business growth.
Can AI analyze Google reviews?
Yes, AI can analyze Google reviews in several ways. However, most methods involve keyword-based analysis that isn’t very insightful. RightResponse AI thoroughly analyzes Google reviews through an AI-powered sophisticated sentiment analysis engine, allowing you to see how your business locations perform within a set of customized Topics and Categories. Learn more about the broader topic of Sentiment Analysis by reading our Definitive Guide to Sentiment Analysis.
Our % positive mentions metric breaks down your reviews by those topics and categories, highlighting which services, products, and/or categories are most well-received by your customers and showing which areas need focus for improvement.
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Frequently Asked Questions
Sentiment analysis for Google reviews involves identifying and categorizing customer attitudes—positive or negative—within the text. Unlike simple star ratings or keyword counts, it evaluates the actual wording customers use and interprets sentiment tied to specific topics. This helps businesses understand not just how many reviews they receive, but what customers feel about aspects of their service—enabling data-driven improvements in operations, customer experience, and overall reputation.
Keyword-based analysis counts how often certain words appear but doesn’t interpret tone or emotional context. In contrast, sentiment analysis examines each mention to determine if it’s positive or negative—even when keywords are present in mixed sentiment. This adds depth, turning mentions into actionable insights—rather than asking users to manually interpret sentiment per review. It identifies true customer attitudes without reading each review, making review data much more meaningful.
Through sentiment analysis organized by topic and category, businesses can see which aspects customers appreciate or criticize. It delivers insights like the percentage of positive mentions per topic, identifying strengths (e.g. staff friendliness) or weaknesses (e.g. product quality). It also enables comparisons across multiple locations—revealing differences in customer experience—so operations teams can take precise actions to uphold consistently high satisfaction across their footprint.
RightResponse AI uses advanced generative AI to analyze reviews phrase by phrase, assigning both a topic/category and sentiment (positive or negative). It goes beyond keyword counting by extracting meaning from review language, turning unstructured review data into structured insights. Its approach organizes sentiment into customizable categories and topics and quantifies customer sentiment more precisely—transforming raw text into business intelligence that drives improvement.
RightResponse AI automatically suggests draft categories and topics based on your industry, but every category and topic is fully customizable. You can edit or add themes to match your business structure and customer focus. This flexibility ensures sentiment data reflects the real priorities and language of your business, making insights more relevant and easier to act on—especially when aligning feedback analysis with operational goals and decision-making.
RightResponse AI tracks percentage of positive mentions within each sentiment topic, enabling pinpointed analysis of customer satisfaction by parameter. It also supports cross-location comparisons, showing how sentiment in each category varies across multiple business locations. These metrics empower teams to identify strengths and weaknesses at a granular level—enabling targeted, location-specific improvements even when average ratings appear similar across locations.
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