Turn Reviews into Actionable Insights

Why Analyze Google Reviews?

Customer reviews are a goldmine of information. They provide direct customer feedback, enabling you to understand your audience better and improve your service quality. Analyzing Google reviews specifically can lead to strategic decision-making that significantly boosts customer satisfaction.

Data Extraction Made Simple

With Keboola, extracting your data from Google reviews becomes effortless. Our platform seamlessly integrates with MySQL databases, allowing you to access and manage your data efficiently.

1. Connect to MySQL: Using Keboola’s intuitive interface, simply search and select the MySQL connector. Enter your credentials provided in your database settings, test the connection, and you're ready to go.
2. Select Tables: Choose the exact tables you wish to extract, ensuring that you only import relevant data. Keboola enables incremental fetching, meaning you can update your reviews regularly without unnecessary data duplication.
3. Automated Data Loading: Keboola automatically extracts and uploads your data into a Snowflake schema, ensuring your data is always ready for processing.

Transform Your Data with Snowflake SQL

Clean and structure your extracted Google reviews using powerful Snowflake SQL transformations within Keboola:

• Data Cleaning: Easily filter out irrelevant or incomplete reviews.
• Data Subsetting: Select a manageable data subset for faster processing and analysis.
• Automated Execution: Schedule transformations to run automatically, ensuring your data is always fresh and analysis-ready.

Sentiment Analysis and Keywords Extraction with Hugging Face LLM

Keboola integrates seamlessly with Hugging Face's Large Language Models (LLM), empowering you to enrich your data with sentiment analysis and keyword extraction:

• Language Translation: Automatically translate non-English reviews to English for consistent analysis.
• Keyword Extraction: Identify key topics and trends discussed by reviewers to better understand your customers.
• Sentiment Scoring: Obtain valuable insights into customer satisfaction through sentiment scores ranging from highly negative (-1) to highly positive (+1).

Simply select your input data, configure your Hugging Face model, and define your prompt to extract meaningful contextual insights from each review.

Python Parsing for Structured Insights

Use Keboola’s Python transformations to parse complex JSON outputs, structuring them into easily analyzable data tables:

• JSON Parsing: Convert outputs from Hugging Face into structured columns for easier analysis.
• Keyword Aggregation: Quickly identify frequently mentioned keywords or topics.
• Data Structuring: Prepare clean, structured datasets ready for visualization and further analysis.

Visualize Your Data with Streamlit Apps

Bring your insights to life using Streamlit data apps directly integrated into Keboola:

• Customizable Dashboards: Create interactive visualizations, such as word clouds, sentiment distributions, and keyword frequency charts.
• Real-time Data Integration: Connect your visualizations directly to Keboola Storage to ensure your insights are always up-to-date.
• Easy Deployment: Deploy your data app with just a few clicks; no complicated infrastructure management required.

Automate Your Entire Workflow

Keboola’s Flow Builder allows you to automate the entire analysis pipeline:

• Sequential or Parallel Execution: Set up your workflow exactly how you need it, with multiple jobs running sequentially or concurrently.
• Scheduled Runs: Schedule regular data updates and transformations to ensure your insights are fresh and accurate.
• Continuous Deployment: Automatically redeploy your Streamlit apps to guarantee continuous availability of visual insights.

Real-World Example: London Eye Reviews

To illustrate this powerful workflow, we built a complete use case analyzing real-world Google reviews of London's famous attraction, the London Eye. Here's what we achieved:

1. Extracted 5,000 Google reviews from MySQL.
2. Cleaned and structured data using Snowflake SQL.
3. Translated and analyzed sentiment using Hugging Face LLM.
4. Parsed LLM outputs using Python transformations.
5. Deployed an intuitive Streamlit app for visual exploration.

This comprehensive, hands-on approach demonstrates the true power and ease of Keboola, turning customer feedback into actionable insights to drive strategic business decisions.

Why Choose Keboola?

• User-Friendly Interface: No extensive training required. Keboola is intuitive and user-friendly.
• Comprehensive Integration: Easily integrates with MySQL, Snowflake, Hugging Face, Python, and Streamlit.
• End-to-End Automation: Automate data extraction, transformation, analysis, and visualization effortlessly.
• Scalable and Secure: Built on Snowflake, Keboola guarantees scalability and secure data management.

Get Started Today

Don't let valuable customer insights go unnoticed. Start analyzing your Google reviews today with Keboola and transform your customer experience.