AI-Assisted Content Analysis

Restaurant reviews,
decoded

A comparative content analysis of 50 Yelp reviews across two Austin restaurants — using AI to surface what customers actually care about, faster than manual coding ever could.

TypeAI-Assisted Research

ToolsChatGPT-4o, Custom GPT

Scope50 reviews (25 each)

RestaurantsLucky Robot & Cabo Bob's

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01 — Overview

The Approach

Designed a conceptual coding scheme using AI to categorize feedback into Positive, Negative, and Neutral types with sub-codes: Compliments, Complaints, Recommendations. Analyzed patterns across both restaurants and delivered data-backed recommendations.

Method

AI-powered sentiment taxonomy

AI-powered sentiment taxonomy applied consistently across 50 reviews

Output

Data-backed recommendations

Data-backed operational recommendations, not just vibes

02 — Findings

What the data revealed

~36%

Positive compliments across both restaurants

Reviews analyzed (25 each)

Sentiment categories (Positive, Negative, Neutral)

Sub-codes per category

Lucky Robot

Strong front-of-house consistency

Fewer service complaints — strong front-of-house consistency

Cabo Bob's

Bigger opportunity for improvement

More actionable constructive criticism — bigger opportunity for improvement

Shared patterns

Dominant positive signal

Both restaurants showed ~36% positive compliments as the dominant signal

The gap

Unprocessed, not absent

Negative feedback was being ignored, not absent — the data was there, just unprocessed

03 — Deliverables

What was produced

📊

Sentiment Pattern Analysis

Structured breakdown of review sentiment by restaurant.

Data Analysis

💡

Operational Insights

Specific, actionable findings tied to service and food quality.

Recommendations

👥

Staff Training Recommendations

Where training gaps showed up in the review data.

Operations

🔄

Feedback Utilization Strategy

How to systematically use review data going forward.

Strategy

04 — Reflection

What this project proved

This project demonstrated that AI-assisted analysis can process qualitative data at a speed and consistency that manual coding can't match — without losing the nuance.

"The data was always there. Customers were telling these restaurants exactly what they needed to hear. The gap wasn't feedback — it was processing."

— Andre Espinoza

🤖

AI as research tool

AI doesn't replace qualitative analysis — it accelerates it while maintaining consistency

📈

Patterns over opinions

Individual reviews are noisy. Patterns across 50 reviews are signal.

🎯

Actionable > interesting

The deliverable isn't the analysis — it's what the restaurant does next

Interested in data-driven insights?

AI-assisted analysis can surface what matters from qualitative data — faster and more consistently than manual methods.