Feed AI with Google Maps reviews for sentiment analysis
Compass
Pull Google Maps reviews for any business on a schedule and feed them to your LLM to classify sentiment, detect themes, and generate summaries across locations.
Input
Output fields
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The Google Maps Reviews Scraper Actor is ready to run with the inputs already set for "Feed AI with Google Maps reviews for sentiment analysis". Sign up or log in and you'll land in Apify Console with a copy of this exact task.
Start the "Feed AI with Google Maps reviews for sentiment analysis" task
The Google Maps Reviews Scraper Actor will start running based on the input automatically. You can also change all the input fields, like google maps place urls, number of reviews, and more, to match your specific use case.
Receive the "Feed AI with Google Maps reviews for sentiment analysis" output
Monitor the progress in real time. You will be notified as soon as your dataset is complete and ready for review. Each result includes data such as place title, place google maps link, stars, and more.
Integrate into your workflow
The final output is delivered in JSON, CSV, or Excel format, ready to be plugged into your workflow. You can also run the "Feed AI with Google Maps reviews for sentiment analysis" task via API.
Integrate "Feed AI with Google Maps reviews for sentiment analysis" into your workflow
Connect "Feed AI with Google Maps reviews for sentiment analysis" with 100+ integrations, or run it via API or MCP.
Webhook
n8n
Make
Zapier
Airbyte
Keboola
IFTTT
Hubspot
GDrive
Gmail
Apify MCP
GitHub
Slack
LangChain
LlamaIndex
Flowise
Pinecone
OpenAI
Mastra
Clay
The task returns the place title, Google Maps link, star rating, reviewer's name, direct review link, and the original review text.
The task pulls Google Maps reviews for businesses to perform sentiment analysis, detect themes, and generate summaries.
The task requires a Google Maps place URL or place ID for the location from which to scrape reviews.
This task can be scheduled to run automatically in the Apify Console.
The output from this task can be fed into an AI model for classification and analysis of sentiment across multiple locations.