OpenTable-Sentiment-Analysis

Skills: Web Scrapping, Prompt Engineering, Streamlit, Matplotlib

🍽️ Restaurant Review Analyzer

Restaurant Review Analyzer is a multi-page Streamlit web app that scrapes and analyzes restaurant reviews from OpenTable. It uses the Groq LLM API to extract structured insights about food and staff quality from customer feedback.

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🧠 Key Features

• 🔍 Review Dashboard:
  Scrape reviews of a single restaurant, separate comments on food and staff and analyze sentiment on food and staff.

• 📊 Competitor Analysis:
  Compare average ratings between two restaurants using visual graphs.

• 🎯 LLM-Based Analysis:
  Uses structured prompts and LLM output to categorize review content.

• 🧾 Export:
  Saves analysis results as .json and competitor comparison as .jpg.

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🗂️ Project Structure

restaurant-review-analyzer/

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|-- Dashboard.py # Landing page with sidebar navigation

|-- pages

|-- 1_Reviews_Dashboard.py # Single restaurant review dashboard

|-- 2_Competitor_Analysis_Dashboard.py # Compare two restaurants

|-- util.py # Core logic: scraping, prompting, formatting

|-- requirements.txt

|-- LICENSE

|-- README.md # Project instructions (you are here)

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⚙️ Setup Instructions

🔁 1. Clone the Repository

git clone https://github.com/RJ601/OpenTable-Sentiment-Analysis.git

cd OpenTable-Sentiment-Analysis

🧪 2. Create a Virtual Environment (optional but recommended)

python -m venv env

source env/bin/activate # On Windows: env\Scripts\activate

📦 3. Install Dependencies

Install the required packages using:

pip install -r requirements.txt

🔑 4. Set Up Groq API Key

Login & setup your API key from here (https://console.groq.com/keys)

In the file util.py, find this line:

os.environ['GROQ_API_KEY'] = 'your_api_key_here'

Replace 'your_api_key_here' with your actual Groq API key.

⚙️ 5. Optional Configuration

In util.py, you may also edit:

def scrape_reviews(url, start=1, pages=10):

To change how many pages of reviews you want to scrape.

▶️ Running the App

Run the Streamlit app from the terminal:

streamlit run Dashboard.py

You’ll see a sidebar with the following options:

Reviews Dashboard: Analyze one restaurant.

Competitor Analysis Dashboard: Compare two restaurants.

🖼️ Output

The Reviews Dashboard will save a .json file with categorized review data and would display the colour-coded reviews on the streamlit dashboard.

The Competitor Analysis page will generate a .jpg line chart comparing average ratings over time.

📌 Notes

This app is specifically built for OpenTable review URLs.

Selenium is used for dynamic scraping — make sure Chrome is installed and chromedriver is properly managed (via webdriver_manager).

All logic is modularized inside util.py to simplify maintenance and upgrades.

📄 License

This project is licensed under the MIT License.