Google Review Analysis
This project demonstrates how businesses can leverage AI to analyze customer feedback at scale. By integrating the Google Places API with Microsoft Azure AI, we created a system that retrieves customer reviews, processes them using AI-driven sentiment analysis, and generates actionable insights. This proof of concept highlights how businesses with hundreds or thousands of locations—from franchises to manufacturers—can automatically detect trends, identify areas for improvement, and receive weekly AI-generated reports instead of manually sorting through reviews. Whether monitoring brand reputation, customer satisfaction, or competitor insights, this system offers a scalable, cost-effective approach to transforming raw reviews into valuable business intelligence.
How to Use the AI-Powered Review Analysis Tool
To see how AI can analyze customer feedback, follow these simple steps:
Enter a Business Name
- Type the exact name of a business in the search bar.
- Ensure the name is unique to get accurate results.
Find a Local Business
- If you’re unsure of a business name, use our business lookup tool to find one near you:
Find a Local Business
- If you’re unsure of a business name, use our business lookup tool to find one near you:
Try These Sample Businesses
If you’re not sure what to search for, here are some business names to test:- U.S. Women’s Chamber of Commerce
- Mess Hall
- Sheehy Toyota of Stafford Service & Parts Department
- Seasons 52
- HVAC Frontier Inc.
- Lindsay Ford Used Car Super Center
View the Results
- The system will retrieve the most recent 5 reviews for the business.
- An AI-powered analysis will generate a summary of customer feedback based on those reviews.
Use the Insights
- Quickly understand what customers are saying.
- Identify trends, positive highlights, or areas needing improvement.
This tool is a proof of concept designed to show how businesses can automate customer review analysis and uncover valuable insights in seconds!
Collaborators:
Google API Setup: Sagar Neupane Check out his Linkedin profile
Google API Calls and HTML Setup: Lucas Gentry Check out his Linkedin profile
Explanation of Few-Shot Prompting
This chatbot leverages few-shot prompting and Chain-of-Thought, a method where the model is provided with:
Few-shot prompting ensures the AI generates responses that align with the specific role and style provided in the instructions. COT provides the logical structure to summarize and provide sentiment analysis.
01.
The chatbot is explicitly directed to act as a history professor who explains not just what a person did, but why they did it, their societal impact, and the context of their actions.
02.
A detailed example about Richard Nixon is included to demonstrate the expected format and tone.
Example of Few-Shot Prompt Used:
Instruction: You are an AI assistant designed to process Google reviews for a business. Your task is to perform the following steps:
Calculate and Report Average Rating: Sum the total ratings from all reviews. Divide the total by the number of reviews to determine the average rating. Count and Display Ratings:
Count how many reviews are in each star category (1–5 stars).
Sentiment Analysis:
Categorize reviews with 4 and 5 stars as Positive.
Categorize reviews with 3, 2, and 1 stars as Negative.
Generate Summaries:
Summarize feedback from Positive reviews (4 and 5 stars only).
Summarize feedback from Negative reviews (3, 2, and 1 stars).
Recommendations:
Provide actionable suggestions for improving service based on the negative feedback.
Review Formatting:
- Input (User): “Business Name"
- Output (Assistant): A structured response explaining the Cold War, environmental legislation, and the Watergate scandal, highlighting Nixon’s immediate and long-term societal impacts.
This prompt structure ensures clarity, consistency, and relevance in the chatbot’s replies.