Automate Review Collection and Sentiment Analysis

For restaurants, customer feedback is essential for continuous improvement. However, manually collecting reviews and analyzing sentiments can take up too much time. What if there was an easy way to automate review collection, analyze sentiment, and gain actionable insights?

With Protocols, restaurants can streamline the process of tracking customer feedback, saving time and improving customer satisfaction. This guide will show you how to automate customer feedback collection, analyze sentiment, and generate valuable insights—no coding experience required.

MongoDB can be used to securely store feedback data, track trends over time, and provide quick access to insights.

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The Problem: Manual Review Collection and Analysis Is Time-Consuming

Restaurant owners often struggle with:
❌ Manually collecting customer reviews from various platforms
❌ Analyzing customer sentiments manually, which is error-prone
❌ Slow decision-making due to lack of quick insights

Time Wasted Without Automation

Restaurant managers often spend 4 hours per week manually collecting and analyzing customer feedback. At $30/hour, that’s $480 per month spent on a repetitive, manual task.

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The Solution: Automating Review Collection and Sentiment Analysis with Protocols

With Protocols, you can:
✅ Automatically collect customer reviews from different platforms (e.g., Google, Yelp)
✅ Perform sentiment analysis on reviews to identify areas of improvement
✅ Store feedback data securely in MongoDB and track sentiment over time

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Step 1: Collect Customer Feedback

First, we’ll collect customer reviews from an Excel file or CSV file containing the feedback.

Example Review Data (customer_reviews.csv)

Customer ID

Review Date

Rating

Feedback

1001

2025-02-10

5

“Amazing food, great service!”

1002

2025-02-11

3

“Food was good, but the wait time was long.”

1003

2025-02-12

1

“Not happy, the food was cold and bland.”

Let’s load this data into Python.

import pandas as pd  

# Load customer feedback
reviews = pd.read_csv("customer_reviews.csv")

# Display first few rows
print(reviews.head())

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Step 2: Perform Sentiment Analysis on Reviews

We can use a simple sentiment analysis model to categorize the reviews as “Positive”, “Neutral”, or “Negative”. For this, we’ll use a basic sentiment analysis tool from a Python library.

from textblob import TextBlob

# Function to determine sentiment
def analyze_sentiment(feedback):
    analysis = TextBlob(feedback)
    if analysis.sentiment.polarity > 0:
        return "Positive"
    elif analysis.sentiment.polarity == 0:
        return "Neutral"
    else:
        return "Negative"

# Apply sentiment analysis to each review
reviews["Sentiment"] = reviews["Feedback"].apply(analyze_sentiment)

print(reviews[["Customer ID", "Feedback", "Sentiment"]])

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Step 3: Categorize Reviews Based on Rating

We can filter reviews based on the rating and sentiment to help focus on the most critical feedback.

# Filter positive, neutral, and negative reviews
positive_reviews = reviews[reviews["Sentiment"] == "Positive"]
negative_reviews = reviews[reviews["Sentiment"] == "Negative"]
neutral_reviews = reviews[reviews["Sentiment"] == "Neutral"]

# Display filtered reviews
print("Positive Reviews: ", positive_reviews)
print("Negative Reviews: ", negative_reviews)

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Step 4: Store the Data in MongoDB

To track reviews over time and quickly generate insights, we can store all feedback data in MongoDB.

from pymongo import MongoClient

# Connect to MongoDB
client = MongoClient('mongodb://localhost:27017/')
db = client['restaurant_feedback']
collection = db['reviews']

# Insert data into MongoDB
collection.insert_many(reviews.to_dict("records"))

print("Feedback data saved to MongoDB.")

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Step 5: Generate Insights and Reports

Now, we can generate a report showing the percentage of positive, neutral, and negative feedback.

# Calculate percentage of sentiment types
sentiment_counts = reviews["Sentiment"].value_counts(normalize=True) * 100

print("Sentiment Analysis Report: ")
print(sentiment_counts)

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The Result: Real-Time Insights and Automated Review Collection

By automating customer feedback collection and sentiment analysis, restaurants can:
✅ Save 4 hours per week, worth $480/month
✅ Gain immediate insights into customer sentiment
✅ Store feedback securely in MongoDB and track trends over time

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I Can Automate This, So You Can Focus on Your Customers

Automating customer feedback collection and sentiment analysis means you can respond faster to both positive and negative reviews. I can build a system that collects, analyzes, and stores feedback data for you, saving you time and allowing you to improve customer experience effortlessly.

Let me automate your review system—contact me today!