# Google Maps Reviews Pro - Sentiment, Topics, Competitor Delta (`seibs.co/google-maps-reviews-pro`) Actor Google Maps reviews with built-in intelligence: per-review sentiment, topic clustering (food/service/wait/cleanliness/value), responder tracking, time-series trend, competitor delta, LLM-ready output. Multi-business batch input. For reputation managers, local SEO, chains. - **URL**: https://apify.com/seibs.co/google-maps-reviews-pro.md - **Developed by:** [Seibs.co](https://apify.com/seibs.co) (community) - **Categories:** Business, Marketing, AI - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, NaN bookmarks - **User rating**: No ratings yet ## Pricing from $10.00 / 1,000 business summaries This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events. Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#pay-per-event ## What's an Apify Actor? Actors are a software tools running on the Apify platform, for all kinds of web data extraction and automation use cases. In Batch mode, an Actor accepts a well-defined JSON input, performs an action which can take anything from a few seconds to a few hours, and optionally produces a well-defined JSON output, datasets with results, or files in key-value store. In Standby mode, an Actor provides a web server which can be used as a website, API, or an MCP server. Actors are written with capital "A". ## How to integrate an Actor? If asked about integration, you help developers integrate Actors into their projects. You adapt to their stack and deliver integrations that are safe, well-documented, and production-ready. The best way to integrate Actors is as follows. In JavaScript/TypeScript projects, use official [JavaScript/TypeScript client](https://docs.apify.com/api/client/js.md): ```bash npm install apify-client ``` In Python projects, use official [Python client library](https://docs.apify.com/api/client/python.md): ```bash pip install apify-client ``` In shell scripts, use [Apify CLI](https://docs.apify.com/cli/docs.md): ````bash # MacOS / Linux curl -fsSL https://apify.com/install-cli.sh | bash # Windows irm https://apify.com/install-cli.ps1 | iex ```bash In AI frameworks, you might use the [Apify MCP server](https://docs.apify.com/platform/integrations/mcp.md). If your project is in a different language, use the [REST API](https://docs.apify.com/api/v2.md). For usage examples, see the [API](#api) section below. For more details, see Apify documentation as [Markdown index](https://docs.apify.com/llms.txt) and [Markdown full-text](https://docs.apify.com/llms-full.txt). # README ## Google Maps Reviews Pro Multi-business Google Maps reviews scraper with built-in sentiment, topic clustering, responder tracking, time-series trend, and competitor delta analysis. ### What does Google Maps Reviews Pro do? It wraps the popular `compass/Google-Maps-Reviews-Scraper` upstream and layers an analysis pass on top: per-review sentiment with negation handling, topic tagging across nine common categories, owner-response stats, 12-month time-series with `improving / flat / declining` trend label, and pairwise competitor delta. Optional LLM-ready markdown summaries drop straight into agent prompts. ### AI / RAG / Agent Built for AI reputation-management agents and local-SEO bots. Set `output_format=llm_ready` to get a pre-summarized Markdown block per business (rating trend, top complaints, top praise, response gap, competitor delta) that a model can ingest in a single prompt. Per-review records carry `sentiment`, `topics`, and `is_owner_response` as embedding metadata. Compatible with **LangChain**, **LlamaIndex**, **Pinecone**, **Weaviate**, **Chroma**, and any **MCP**-aware agent runtime. ```python from apify_client import ApifyClient from langchain.schema import Document from langchain_community.vectorstores import Weaviate from langchain_openai import OpenAIEmbeddings client = ApifyClient("APIFY_TOKEN") run = client.actor("you/google-maps-reviews-pro").call(run_input={ "placeIds": ["ChIJN1t_tDeuEmsRUsoyG83frY4", "ChIJrTLr-GyuEmsRBfy61i59si0"], "maxReviews": 200, "output_format": "llm_ready", "includeCompetitorDelta": True, }) docs = [ Document( page_content=item["llm_summary"], metadata={ "place_id": item["place_id"], "name": item["name"], "rating": item["rating"], "trend": item["trend_label"], "top_complaints": item["top_complaint_topics"], }, ) for item in client.dataset(run["defaultDatasetId"]).iterate_items() ] Weaviate.from_documents(docs, OpenAIEmbeddings(), index_name="ReputationRAG") ```` ### Features - Per-review sentiment (positive / neutral / negative) with `sentiment_score` in \[-1, 1] - lexicon-based, no API key, no per-call cost. - Topic clustering - `food_quality`, `service`, `wait_time`, `cleanliness`, `value_pricing`, `ambience`, `staff_friendliness`, `parking`, `accessibility`, aggregated into `topic_distribution` and ranked into `top_complaint_topics` / `top_praise_topics`. - Responder tracking - response rate, average time-to-respond in days, owner reply sentiment distribution. - Time-series breakdown - last 12 months of review counts + average rating, with derived `recent_trend`. - Competitor delta mode - given 2-5 businesses, returns pairwise rating delta, review-count delta, common complaints, unique complaints per side. - LLM-ready output - set `output_format=llm_ready` and every record gets a `llm_summary_md` markdown block. - Reviewer profile signals - local-guide flag, total reviews submitted, photo-uploader pattern (shill detection). ### Use cases - Chain reputation managers monitoring 20-500 locations who currently glue together a raw review scrape + sentiment notebook + dashboard. - Local SEO agencies running monthly client reports - drop the LLM markdown straight into the report template. - Restaurant groups / multi-location service businesses with response-rate / time-to-respond / recent-trend KPIs. - Competitive intel teams - `competitor_delta` answers "what do customers complain about us vs the competition?" - AI product builders feeding local-business data into LLM workflows who need pre-summarized markdown, not raw JSON. ### FAQ **Q: Is this legal?** A: Yes - reviews are public on Google Maps and we go through the upstream `compass/Google-Maps-Reviews-Scraper`, which scrapes the public Maps frontend (not the paid Google Places API). Use the data per Google's Terms of Service and applicable law. **Q: Why might a run fail?** A: (1) Upstream scrape fails when a place is not found or Maps blocks the session - the row comes back with `available: false` and a `reason`. (2) Malformed reviews crash the analysis pass - same handling. (3) Pushing `max_reviews_per_business` above 500 on many businesses at once triggers rate limits - lower concurrency or split the run. **Q: How fresh is the data?** A: Live at crawl time. Reviews are read directly from the public Maps page during the run. `review_sort: newest` returns most recent first - typically within minutes of being posted. **Q: Can I schedule this daily or weekly?** A: Yes - weekly is the standard cadence for chain reputation monitoring (20-500 locations). Daily for crisis-watch on a single high-volume business. Use Apify Schedules; combine with `recent_trend` to alert on `declining` flips. **Q: How do I push results into a CRM or BI tool?** A: Two paths. (1) `output_format: csv_friendly` flattens reviews for direct import into BI dashboards (Looker, Power BI, Sheets). (2) `output_format: llm_ready` drops `llm_summary_md` straight into agent prompts or a client report template. Zapier/Make/n8n forward business-summary records to HubSpot, Salesforce, or a Slack channel on negative-trend alerts. **Q: How is this different from `compass/google-maps-reviews`?** A: `compass/google-maps-reviews` is the upstream raw-scraper layer - it pulls reviews and exits. This actor wraps that scrape and layers an intelligence pass on top: per-review sentiment with negation handling, 9-topic clustering with aggregated `top_complaint_topics` / `top_praise_topics`, owner-responder metrics (response rate, time-to-respond, reply sentiment), 12-month time-series with `improving / flat / declining` trend label, pairwise competitor delta, and LLM-ready markdown summaries. You are paying for the analysis layer, not the scrape - if all you need is raw reviews, use the upstream directly. **Q: How accurate is the sentiment classifier?** A: ~85% on English consumer reviews against human labels in spot-check sets; lexicon-based with 2-token negation lookback. Non-English (es, fr, de) runs ~70-75%. **Q: How does PPE pricing actually work here?** A: $0.010 per `business_summary`, $0.001 per `review_record`, $0.020 per `competitor_delta_record`, $0.005 per `llm_summary`. A 100-review business in JSON mode is about $0.11; a 5-business competitor delta with 100 reviews each is about $0.65. ### Related Actors - Pair with ANY lead-finder actor (`home-services-lead-finder`, `houzz-contractor-leads`, `contract-heavy-smb-lead-finder`, `shopify-store-discovery`, etc.) - those build the lead list, this actor monitors each lead's reputation as a companion intelligence layer. - [`b2b-sales-triggers`](../b2b-sales-triggers/) - layer company-level intent (hiring, funding, exec changes) on top of review-driven reputation context. - [`reddit-topic-watcher`](../reddit-topic-watcher/) - extend reputation monitoring beyond Google Maps to Reddit complaint and praise threads. ### Integrations - Zapier - push to HubSpot/Salesforce/Pipedrive/Apollo/Klaviyo - Make.com - workflow automation - n8n - self-hosted automation - Apify webhooks - POST to your endpoint - API + dataset export (JSON/CSV/Excel/XML) - MCP / AI agents - call from Claude/GPT/LangChain ### Modes | Mode | What it does | Inputs | |---|---|---| | `batch_analysis` | Independent analysis of N businesses (up to 50). | List of URLs / place IDs / names. | | `competitor_delta` | Pairwise comparison of 2-5 businesses. | 2-5 inputs. | | `single_business_deep` | Max depth on one business; bumps `max_reviews` to 500+ and forces reviewer profiles on. | First input only. | ### Input See `.actor/INPUT_SCHEMA.json`. Sample: ```json { "mode": "batch_analysis", "business_inputs": [ "https://maps.google.com/?cid=12345...", "ChIJN1t_tDeuEmsRUsoyG83frY4", "Joe's Pizza, Bleecker St, NYC" ], "max_reviews_per_business": 100, "review_sort": "newest", "include_sentiment_analysis": true, "include_topic_clustering": true, "include_time_series": true, "output_format": "json", "concurrency": 4 } ``` ### Output **Sample output:** [`.actor/sample-output.json`](./.actor/sample-output.json) — copy-paste-ready preview of real-looking records. First record inline: ```json { "record_type": "business_summary", "place_id": "ChIJN1t_tDeuEmsRUsoyG83frY4", "business_name": "Smiles Dental of Austin", "address": "4218 Bull Creek Rd, Austin, TX 78731", "category": "Dentist", "total_review_count": 412, "current_rating": 4.7, "scraped_at": "2026-05-13T18:14:22Z", "mode": "single_business_deep", "available": true, "reason": null, "sentiment_distribution": { "positive_count": 318, "neutral_count": 51, "negative_count": 43, "positive_pct": 77.2, "negative_pct": 10.4 }, "topic_distribution": { "wait_times": { "count": 87, "avg_rating": 3.4 }, "front_desk": { "count": 142, "avg_rating": 4.8 }, "billing": { "count": 39, "avg_rating": 2.9 }, "cleanliness": { "count": 121, "avg_rating": 4.9 }, "pain_management": { "count": 64, "avg_rating": 4.6 }, "kid_friendly": { "count": 58, "avg_rating": 4.8 } }, "responder_metrics": { "responses_count": 287, "response_rate": 0.696, "avg_response_time_days": 2.4, "owner_sentiment_distribution": { "positive_count": 241, "neutral_count": 38, "negative_count": 8 } }, "time_series": [ { "month": "2026-04", "review_count": 18, "avg_rating": 4.8 }, { "month": "2026-03", "review_count": 22, "avg_rating": 4.6 }, { "month": "2026-02", "review_count": 15, "avg_rating": 4.5 }, { "month": "2026-01", "review_count": 19, "avg_rating": 4.7 }, { "month": "2025-12", "review_count": 14, "avg_rating": 4.4 } ], "top_complaint_topics": [ "billing", "wait_times" ], "top_praise_topics": [ "cleanliness", "front_desk", "kid_friendly" ], "recent_trend": "improving", "llm_summary_md": "### Smiles Dental of Austin \u2014 Review Summary\n\n**4.7 stars / 412 reviews / 77% positive**\n\nPraised for cleanliness, friendly front desk, and kid-friendly atmosphere. The most consistent complaint is around billing \u2014 39 reviews mention surprise charges or insurance disputes, and the topic averages only 2.9 stars. Wait times are a secondary friction point.\n\nTrend is improving over the last 30 days vs the trailing 90.", "reviews": [ { "review_id": "r-882041", "reviewer_name": "Hannah K.", "reviewer_is_local_guide": true, "reviewer_review_count": 84, "rating": 5.0, "text": "Dr. Patel and the hygienists are amazing with my anxious 6-year-old. Best dental experience our family has had.", "date": "2026-05-08", "helpful_count": 4, "review_topics": [ "kid_friendly", "pain_management" ], "sentiment": "positive", "sentiment_score": 0.82, "owner_response_text": "Thank you Hannah! We love seeing your family.", "owner_response_date": "2026-05-09", "owner_response_sentiment": "positive", "language": "en", "contains_photo": false, "contains_emoji": false }, { "review_id": "r-880014", "reviewer_name": "Greg M.", "reviewer_is_local_guide": false, "reviewer_review_count": 12, "rating": 2.0, "text": "Got hit with a $340 bill they said insurance would cover. Front desk has no answers. Won't be back.", "date": "2026-05-02", "helpful_count": 2, "review_topics": [ "billing" ], "sentiment": "negative", "sentiment_score": -0.71, "owner_response_text": "Greg, we apologize for the confusion. Our office manager will reach out today to review your statement.", "owner_response_date": "2026-05-02", "owner_response_sentiment": "neutral", "language": "en", "contains_photo": false, "contains_emoji": false }, { "review_id": "r-877221", "reviewer_name": "Priya N.", "reviewer_is_local_guide": false, "reviewer_review_count": 31, "rating": 4.0, "text": "Quality of care is excellent. The 35-min wait past my appointment time was frustrating though.", "date": "2026-04-19", "helpful_count": 1, "review_topics": [ "wait_times", "cleanliness" ], "sentiment": "neutral", "sentiment_score": 0.18, "owner_response_text": null, "owner_response_date": null, "owner_response_sentiment": null, "language": "en", "contains_photo": false, "contains_emoji": false } ], "comparison_pairs": null } ``` One record per business with the analysis layer attached. Sample: ```json { "record_type": "business_summary", "business_name": "Joe's Pizza", "current_rating": 4.6, "total_review_count": 8421, "sentiment_distribution": {"positive_pct": 0.78, "negative_pct": 0.09}, "top_praise_topics": ["food_quality", "value_pricing"], "top_complaint_topics": ["wait_time"], "responder_metrics": {"response_rate": 0.42, "avg_response_time_days": 1.8}, "recent_trend": "improving", "reviews": [ {"rating": 5, "text": "Best slice in NY", "sentiment": "positive", "review_topics": ["food_quality"]} ], "available": true, "scraped_at": "2026-05-14T12:00:00Z" } ``` ### Pricing Pay-per-event: | Event | Price | When charged | |---|---|---| | `business_summary` | $0.010 | Once per business successfully analyzed. | | `review_record` | $0.001 | Once per individual review extracted. | | `competitor_delta_record` | $0.020 | Once per pairwise comparison. | | `llm_summary` | $0.005 | Once per business when `output_format=llm_ready`. | Typical 100-review business in JSON mode: $0.11. 5-business `competitor_delta` with 100 reviews each: $0.65. ### FAQ **Q: Does this cost extra Google Places API credits?** A: No - we use the upstream `compass/Google-Maps-Reviews-Scraper` actor (which scrapes the public Maps frontend), not the paid Google Places API. **Q: How accurate is the sentiment classifier?** A: ~85% accuracy on English consumer reviews against human labels in our spot-check sets. Lexicon-based with negation lookback (`not great` flips). Non-English performance is degraded - sentiment on `es`, `fr`, `de` lexicons is roughly 70-75%. **Q: Why are some businesses returning `available=false`?** A: Either the upstream scrape failed (place not found / Maps blocked the session) or our analysis pass crashed on a malformed review. The `reason` field tells you which. **Q: Can I get one row per review for a CSV export?** A: Yes - either set `output_format=csv_friendly` (flattens at write time) or use the `Reviews (one per row)` view tab in the dataset preview, which unwinds the reviews array. ### Support Open an issue on the actor's GitHub or contact via Apify Store. Include the run ID and input config. ### Changelog See [CHANGELOG.md](./CHANGELOG.md). # Actor input Schema ## `mode` (type: `string`): batch\_analysis = analyze N businesses independently. competitor\_delta = pairwise side-by-side comparison of 2-5 businesses. single\_business\_deep = max-depth on one business (largest review pull, full per-review enrichment). ## `business_inputs` (type: `array`): Up to 50 Google Maps URLs, place IDs (ChIJ...), or freeform business names. Mixed types accepted. Names are resolved by the upstream Maps actor. ## `max_reviews_per_business` (type: `integer`): Cap on how many reviews to pull per business from the upstream scraper. Higher = better stats but slower and more expensive upstream cost. ## `review_sort` (type: `string`): How Google Maps should sort reviews before we take the first N. 'newest' is best for trend analysis; 'lowest\_rated' is best for surfacing complaints fast. ## `language` (type: `string`): Language code for reviews. Sentiment and topic lexicons currently work best for English; other languages still return raw reviews but may have lower analysis accuracy. ## `include_owner_responses` (type: `boolean`): When true, captures owner replies on each review and computes responder metrics (response rate, time-to-response, owner reply sentiment). Strong reputation-management signal. ## `include_sentiment_analysis` (type: `boolean`): Lexicon-based sentiment scoring (positive / neutral / negative) with negation handling. No API key needed - runs entirely in-actor. ## `include_topic_clustering` (type: `boolean`): Detects topics in each review using keyword sets: food\_quality, service, wait\_time, cleanliness, value\_pricing, ambience, staff\_friendliness, parking, accessibility. ## `include_time_series` (type: `boolean`): Monthly review count + average rating for the last 12 months. Determines trend (improving / flat / declining). ## `include_reviewer_profiles` (type: `boolean`): For each review, flag local guide status, # of reviews submitted, photo-uploader pattern. Slower (deeper scrape) but useful for spotting suspicious / shill reviews. ## `output_format` (type: `string`): json = full structured business + reviews records. llm\_ready = adds a markdown summary block per business optimized for LLM prompt injection. csv\_friendly = flat one-row-per-review (no nested arrays). ## `competitor_delta_max_pairs` (type: `integer`): Only used in competitor\_delta mode. Caps the number of pairwise comparisons emitted. With 5 businesses there are 10 pairs; this caps that to the most-divergent N. ## `apify_proxy_groups` (type: `array`): Proxy groups passed through to the compass/Google-Maps-Reviews-Scraper upstream actor. RESIDENTIAL is most reliable for Google Maps. ## `concurrency` (type: `integer`): Number of businesses processed in parallel. Each business triggers one upstream actor call; raise carefully. ## Actor input object example ```json { "mode": "batch_analysis", "business_inputs": [ "https://www.google.com/maps/place/?q=place_id:ChIJN1t_tDeuEmsRUsoyG83frY4" ], "max_reviews_per_business": 100, "review_sort": "newest", "language": "en", "include_owner_responses": true, "include_sentiment_analysis": true, "include_topic_clustering": true, "include_time_series": true, "include_reviewer_profiles": false, "output_format": "json", "competitor_delta_max_pairs": 5, "apify_proxy_groups": [ "RESIDENTIAL" ], "concurrency": 4 } ``` # Actor output Schema ## `datasetItems` (type: `string`): Narrow, token-efficient slice of every record. Consumer: LLM agents (Claude, GPT, LangChain tools), MCP hosts, dashboards. Review fields: business, rating, review count, sentiment distribution, top topics, response rate, recent trend. ## `datasetItemsDetailed` (type: `string`): All fields for every record. Consumer: humans browsing the dataset in the Apify UI, RAG ingest pipelines, and full backups. Larger payload — not recommended as a direct LLM tool response. ## `datasetItemsUnwind` (type: `string`): Same records, but each element of the `reviews` array is promoted to its own row. Consumer: Zapier / Make / n8n / Google Sheets users who want one row per item, and LLM agents that prefer flat rows over nested arrays. ## `datasetItemsMcp` (type: `string`): First 50 overview records as a clean JSON array. Wrap on the agent side in an MCP tool-call response envelope, e.g. `{ "ok": true, "data": , "meta": { "actor": "google-maps-reviews-pro", "count": , "view": "overview" } }`. Consumer: MCP servers, Claude Desktop, Cursor, OpenAI Assistants tool calls. ## `datasetItemsCsv` (type: `string`): Spreadsheet-friendly export of the overview view. Consumer: humans, sales-ops teams, Excel / Google Sheets users. # API You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup. ## JavaScript example ```javascript import { ApifyClient } from 'apify-client'; // Initialize the ApifyClient with your Apify API token // Replace the '' with your token const client = new ApifyClient({ token: '', }); // Prepare Actor input const input = { "mode": "batch_analysis", "business_inputs": [ "https://www.google.com/maps/place/?q=place_id:ChIJN1t_tDeuEmsRUsoyG83frY4" ] }; // Run the Actor and wait for it to finish const run = await client.actor("seibs.co/google-maps-reviews-pro").call(input); // Fetch and print Actor results from the run's dataset (if any) console.log('Results from dataset'); console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${run.defaultDatasetId}`); const { items } = await client.dataset(run.defaultDatasetId).listItems(); items.forEach((item) => { console.dir(item); }); // 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/js/docs ``` ## Python example ```python from apify_client import ApifyClient # Initialize the ApifyClient with your Apify API token # Replace '' with your token. client = ApifyClient("") # Prepare the Actor input run_input = { "mode": "batch_analysis", "business_inputs": ["https://www.google.com/maps/place/?q=place_id:ChIJN1t_tDeuEmsRUsoyG83frY4"], } # Run the Actor and wait for it to finish run = client.actor("seibs.co/google-maps-reviews-pro").call(run_input=run_input) # Fetch and print Actor results from the run's dataset (if there are any) print("💾 Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"]) for item in client.dataset(run["defaultDatasetId"]).iterate_items(): print(item) # 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start ``` ## CLI example ```bash echo '{ "mode": "batch_analysis", "business_inputs": [ "https://www.google.com/maps/place/?q=place_id:ChIJN1t_tDeuEmsRUsoyG83frY4" ] }' | apify call seibs.co/google-maps-reviews-pro --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=seibs.co/google-maps-reviews-pro", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Google Maps Reviews Pro - Sentiment, Topics, Competitor Delta", "description": "Google Maps reviews with built-in intelligence: per-review sentiment, topic clustering (food/service/wait/cleanliness/value), responder tracking, time-series trend, competitor delta, LLM-ready output. Multi-business batch input. For reputation managers, local SEO, chains.", "version": "0.1", "x-build-id": "VOcsSGzxvKd0xUK1I" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/seibs.co~google-maps-reviews-pro/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-seibs.co-google-maps-reviews-pro", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for its completion, and returns Actor's dataset items in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/seibs.co~google-maps-reviews-pro/runs": { "post": { "operationId": "runs-sync-seibs.co-google-maps-reviews-pro", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK", "content": { "application/json": { "schema": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/seibs.co~google-maps-reviews-pro/run-sync": { "post": { "operationId": "run-sync-seibs.co-google-maps-reviews-pro", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } } }, "components": { "schemas": { "inputSchema": { "type": "object", "required": [ "mode", "business_inputs" ], "properties": { "mode": { "title": "Run mode", "enum": [ "batch_analysis", "competitor_delta", "single_business_deep" ], "type": "string", "description": "batch_analysis = analyze N businesses independently. competitor_delta = pairwise side-by-side comparison of 2-5 businesses. single_business_deep = max-depth on one business (largest review pull, full per-review enrichment).", "default": "batch_analysis" }, "business_inputs": { "title": "Businesses (URLs, place IDs, or names)", "type": "array", "description": "Up to 50 Google Maps URLs, place IDs (ChIJ...), or freeform business names. Mixed types accepted. Names are resolved by the upstream Maps actor.", "items": { "type": "string" } }, "max_reviews_per_business": { "title": "Max reviews per business", "minimum": 10, "maximum": 1000, "type": "integer", "description": "Cap on how many reviews to pull per business from the upstream scraper. Higher = better stats but slower and more expensive upstream cost.", "default": 100 }, "review_sort": { "title": "Review sort order", "enum": [ "newest", "highest_rated", "lowest_rated", "most_relevant" ], "type": "string", "description": "How Google Maps should sort reviews before we take the first N. 'newest' is best for trend analysis; 'lowest_rated' is best for surfacing complaints fast.", "default": "newest" }, "language": { "title": "Review language", "enum": [ "en", "es", "fr", "de", "it", "pt", "ja", "ko", "zh" ], "type": "string", "description": "Language code for reviews. 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