# Yelp Reviews Scraper - Sentiment, Topics, Competitor Delta (`seibs.co/yelp-reviews-pro`) Actor Bulk Yelp review scraper with per-review sentiment, topic clusters (food, service, wait, value, parking), responder tracking, 12-month trend and competitor delta. LLM-ready JSON for reputation, local SEO and chain ops teams. - **URL**: https://apify.com/seibs.co/yelp-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 ## Yelp Reviews Pro > **TL;DR for local SEO agencies, multi-unit franchise managers, and reputation-management teams:** Pulls Yelp reviews for one or many businesses with built-in sentiment, 9-topic clustering, owner-response tracking, 12-month trend label, and pairwise competitor delta. Compared to a generic Yelp scraper, you get an intelligence layer on top (sentiment with negation handling, topic tags, response-gap metric, competitor delta, and an llm_ready Markdown summary mode for AI agents). Free Apify plan covers small business-input runs on your $5 platform credit. PPE charges scale per review. Upgrade to Apify Starter ($49/mo) for production volume. ### Run it in 30 seconds ```python ## Via the Apify Python SDK from apify_client import ApifyClient client = ApifyClient("") run = client.actor("seibs.co/yelp-reviews-pro").call(run_input={ "mode": "single_business", "business_inputs": [ "https://www.yelp.com/biz/the-french-laundry-yountville" ], "max_reviews_per_business": 200, "include_topic_clustering": true }) for item in client.dataset(run["defaultDatasetId"]).iterate_items(): print(item) ```` Or via curl: ```bash curl -X POST "https://api.apify.com/v2/acts/seibs.co~yelp-reviews-pro/run-sync-get-dataset-items?token=" \ -H "Content-Type: application/json" \ -d '{"mode": "single_business", "business_inputs": ["https://www.yelp.com/biz/the-french-laundry-yountville"], "max_reviews_per_business": 200, "include_topic_clustering": true}' ``` Or click "Try for free" on this page if you prefer the no-code UI. ### What you get Each run produces: - A clean dataset, filterable in the Apify console and downloadable as CSV or JSON - An OUTPUT.html dashboard preview of your top records - A sample-output preview at [`.actor/sample-output.json`](./.actor/sample-output.json) Per-archetype custom artifacts shipped with this actor: - top-negative-reviews.html (with suggested response templates and copy-to-clipboard buttons) - sentiment-trend.csv (12-month trend with improving / flat / declining label) - competitor-delta.csv (pairwise rating, response-rate, and top-complaint diff) *** ### What does Yelp Reviews Pro do? It wraps the `agents/yelp-reviews` upstream actor 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`, `review_topics`, `reviewer_is_elite`, and `useful_count` as embedding metadata. Compatible with **LangChain**, **LlamaIndex**, **Pinecone**, **Weaviate**, **Chroma**, and any **MCP**-aware agent runtime. ```python from apify_client import ApifyClient client = ApifyClient("APIFY_TOKEN") run = client.actor("you/yelp-reviews-pro").call(run_input={ "mode": "batch_analysis", "business_inputs": [ "https://www.yelp.com/biz/joes-pizza-new-york-9", "https://www.yelp.com/biz/prince-street-pizza-new-york" ], "max_reviews_per_business": 200, "review_sort": "newest", "output_format": "llm_ready" }) for item in client.dataset(run["defaultDatasetId"]).iterate_items(): if item.get("record_type") == "business_summary": print(item["llm_summary_md"]) ``` ### 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 - business-owner 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. - Yelp-native reviewer signals - `reviewer_is_elite`, `reviewer_review_count`, `reviewer_friend_count`, `reviewer_photo_count` (shill / credibility weighting). - Review reactions - `useful_count`, `funny_count`, `cool_count` per review. ### Use cases - Chain reputation managers monitoring 20-500 Yelp 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 - Yelp reviews are public and we go through the upstream `agents/yelp-reviews` actor, which scrapes the public Yelp frontend (not the paid Yelp Fusion API). Use the data per Yelp's Terms of Service and applicable law. **Q: Why might a run fail?** A: (1) Yelp's anti-bot blocks the session - the row comes back with `available: false` and a `reason`. RESIDENTIAL proxy is mandatory; datacenter IPs are rejected. (2) Business URL has no `/biz/` segment - upstream returns nothing. (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 Yelp 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. 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 `agents/yelp-reviews` or `tri_angle/yelp-review-scraper`?** A: Those are the upstream raw-scraper layer - they pull reviews and exit. 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`, `restaurants-lead-finder`, `salon-spa-lead-finder`, etc.) - those build the lead list, this actor monitors each lead's Yelp reputation as a companion intelligence layer. - `google-maps-reviews-pro` - same intelligence layer applied to Google Maps reviews. Run both for cross-platform sentiment + topic + responder coverage. - `reddit-topic-watcher` - extend reputation monitoring beyond review platforms 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 Yelp URLs or business IDs. | | `competitor_delta` | Pairwise comparison of 2-5 businesses. | 2-5 inputs. | | `single_business_deep` | Max depth on one business; bumps `max_reviews` to 500+. | First input only. | ### Input See `.actor/INPUT_SCHEMA.json`. Sample: ```json { "mode": "batch_analysis", "business_inputs": [ "https://www.yelp.com/biz/joes-pizza-new-york-9", "prince-street-pizza-new-york" ], "max_reviews_per_business": 100, "review_sort": "newest", "include_sentiment": true, "include_topic_clustering": true, "include_time_series": true, "output_format": "json", "apify_proxy_groups": ["RESIDENTIAL"], "concurrency": 4 } ``` ### Output One record per business with the analysis layer attached. Sample: ```json { "record_type": "business_summary", "business_name": "Joe's Pizza", "yelp_url": "https://www.yelp.com/biz/joes-pizza-new-york-9", "current_rating": 4.5, "total_review_count": 8421, "sentiment_distribution": {"positive_pct": 78.0, "negative_pct": 9.0}, "top_praise_topics": ["food_quality", "value_pricing"], "top_complaint_topics": ["wait_time"], "responder_metrics": {"response_rate": 12.0, "avg_response_time_days": 4.8}, "recent_trend": "improving", "reviews": [ { "rating": 5, "text": "Best slice in NY", "sentiment": "positive", "sentiment_score": 0.82, "review_topics": ["food_quality"], "reviewer_is_elite": true, "useful_count": 12, "funny_count": 2, "cool_count": 4 } ], "available": true, "scraped_at": "2026-05-16T12:00:00Z" } ``` ### 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. ### Save your input as an Apify Task Apify Tasks let you save a configured input once and re-run it with a single click - no need to re-type search terms, locations, filters, or tier settings every time. Tasks are the foundation for everything that comes next: schedules, monitor mode, and webhook routing all attach to a saved Task, not to the raw actor. Steps to save your current input as a Task: 1. On this actor's Apify Store page, click `Run` with your input fully configured. 2. Click the `Save as task` button at the top of the run page. 3. Name the task something memorable (e.g. `Reviews for top 10 competitors - weekly`). 4. Reload the task page and click `Start` anytime to re-run with the same inputs. Tasks unlock the next two features below: scheduling and monitor mode. ### Run this weekly with Apify Schedules Apify Schedules cron-run any saved Task automatically. Pair this with the saved Task above and you get hands-off recurring runs with no manual clicks, no missed weeks, and a steady stream of fresh data into your CRM or warehouse. Steps to schedule a Task: 1. Save your input as a Task (see above). 2. Go to https://console.apify.com/schedules and click `Create new schedule`. 3. Pick your Task and set the cron expression. Common patterns: - Daily at 9am UTC: `0 9 * * *` - Weekly on Mondays at 9am: `0 9 * * 1` - Monthly on the 1st: `0 9 1 * *` 4. Save. Apify will run your Task on that schedule automatically, push the dataset to whatever integrations you have wired up, and fire run-completion webhooks for downstream automation. Run weekly to track sentiment trends, catch negative reviews fast, and feed fresh review text into your VOC pipeline. ### Monitor mode (v2, beta) Monitor mode is the v2 evolution of this actor and is currently in BETA. It turns a recurring schedule into a true change-feed instead of a firehose of duplicate records. How it works: - When this actor runs under an Apify Schedule, monitor mode is enabled automatically. - Instead of emitting ALL records every run, it emits ONLY records that are NEW or CHANGED since the last scheduled run. - A digest record summarizes the delta (X new, Y changed, Z removed) at the top of every run. - Optional: provide a Slack or email webhook URL in the `monitor_webhook_url` input field and the digest fires there too, so your team gets the delta in their inbox or channel without polling the dataset. - Cost: a single `scheduled_delta_run` event ($0.05) per scheduled run, plus standard PPE on emitted delta records only. Predictable monthly cost, no surprise bills from re-charging for unchanged records. Monitor mode is rolling out to the top 3 actors first (this one included if it's hotel-motel-lead-finder, google-maps-reviews-pro, or mcp-accounting-firm-leads). Full portfolio coverage by end of June. ### 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). ### Found this useful? If this actor saved you time or money, please consider leaving a quick review on the Apify Store. Reviews help other buyers find work that solves their problem and let me prioritize the features paying customers actually use. Leave a review: https://apify.com/seibs.co/yelp-reviews-pro#reviews # 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 Yelp business URLs (https://www.yelp.com/biz/) or business IDs (the slug after /biz/). Mixed types accepted. ## `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. Hard cap of 500 to prevent runaway cost. ## `review_sort` (type: `string`): How the upstream actor should sort reviews before we take the first N. 'newest' is best for trend analysis; 'rating\_low\_high' is best for surfacing complaints fast; 'elites\_first' prioritizes high-credibility reviewers. ## `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 business-owner replies on each review and computes responder metrics (response rate, time-to-response, owner reply sentiment). Strong reputation-management signal. ## `include_sentiment` (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). ## `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 upstream agents/yelp-reviews actor. RESIDENTIAL is mandatory for Yelp - their anti-bot blocks datacenter IPs aggressively. ## `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.yelp.com/biz/joes-pizza-new-york-9" ], "max_reviews_per_business": 100, "review_sort": "newest", "language": "en", "include_owner_responses": true, "include_sentiment": true, "include_topic_clustering": true, "include_time_series": true, "output_format": "json", "competitor_delta_max_pairs": 5, "apify_proxy_groups": [ "RESIDENTIAL" ], "concurrency": 4 } ``` # Actor output Schema ## `datasetItems` (type: `string`): All business summary records with sentiment + topic aggregates. ## `datasetItemsDetailed` (type: `string`): Every aggregate field per business, including time-series and responder metrics. ## `datasetItemsReviews` (type: `string`): Explodes the reviews array - one row per review with sentiment + topics columns. ## `datasetItemsCsv` (type: `string`): Spreadsheet-friendly export of the overview view. # 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.yelp.com/biz/joes-pizza-new-york-9" ] }; // Run the Actor and wait for it to finish const run = await client.actor("seibs.co/yelp-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.yelp.com/biz/joes-pizza-new-york-9"], } # Run the Actor and wait for it to finish run = client.actor("seibs.co/yelp-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.yelp.com/biz/joes-pizza-new-york-9" ] }' | apify call seibs.co/yelp-reviews-pro --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=seibs.co/yelp-reviews-pro", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Yelp Reviews Scraper - Sentiment, Topics, Competitor Delta", "description": "Bulk Yelp review scraper with per-review sentiment, topic clusters (food, service, wait, value, parking), responder tracking, 12-month trend and competitor delta. LLM-ready JSON for reputation, local SEO and chain ops teams.", "version": "0.1", "x-build-id": "RniBGkOpzy6zBPkCJ" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/seibs.co~yelp-reviews-pro/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-seibs.co-yelp-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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/seibs.co~yelp-reviews-pro/runs": { "post": { "operationId": "runs-sync-seibs.co-yelp-reviews-pro", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/seibs.co~yelp-reviews-pro/run-sync": { "post": { "operationId": "run-sync-seibs.co-yelp-reviews-pro", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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 (Yelp URLs or business IDs)", "maxItems": 50, "type": "array", "description": "Up to 50 Yelp business URLs (https://www.yelp.com/biz/) or business IDs (the slug after /biz/). Mixed types accepted.", "items": { "type": "string" } }, "max_reviews_per_business": { "title": "Max reviews per business", "minimum": 10, "maximum": 500, "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. Hard cap of 500 to prevent runaway cost.", "default": 100 }, "review_sort": { "title": "Review sort order", "enum": [ "newest", "oldest", "rating_high_low", "rating_low_high", "elites_first", "yelp_default" ], "type": "string", "description": "How the upstream actor should sort reviews before we take the first N. 'newest' is best for trend analysis; 'rating_low_high' is best for surfacing complaints fast; 'elites_first' prioritizes high-credibility reviewers.", "default": "newest" }, "language": { "title": "Review language", "enum": [ "en", "es", "fr", "de", "it", "pt", "ja", "ko", "zh" ], "type": "string", "description": "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.", "default": "en" }, "include_owner_responses": { "title": "Include owner responses", "type": "boolean", "description": "When true, captures business-owner replies on each review and computes responder metrics (response rate, time-to-response, owner reply sentiment). Strong reputation-management signal.", "default": true }, "include_sentiment": { "title": "Per-review sentiment analysis", "type": "boolean", "description": "Lexicon-based sentiment scoring (positive / neutral / negative) with negation handling. No API key needed - runs entirely in-actor.", "default": true }, "include_topic_clustering": { "title": "Topic clustering per review", "type": "boolean", "description": "Detects topics in each review using keyword sets: food_quality, service, wait_time, cleanliness, value_pricing, ambience, staff_friendliness, parking, accessibility.", "default": true }, "include_time_series": { "title": "Time-series breakdown", "type": "boolean", "description": "Monthly review count + average rating for the last 12 months. Determines trend (improving / flat / declining).", "default": true }, "output_format": { "title": "Output format", "enum": [ "json", "llm_ready", "csv_friendly" ], "type": "string", "description": "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).", "default": "json" }, "competitor_delta_max_pairs": { "title": "Competitor delta max pairs", "minimum": 2, "maximum": 10, "type": "integer", "description": "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.", "default": 5 }, "apify_proxy_groups": { "title": "Proxy groups (forwarded to upstream)", "type": "array", "description": "Proxy groups passed through to the upstream agents/yelp-reviews actor. RESIDENTIAL is mandatory for Yelp - their anti-bot blocks datacenter IPs aggressively.", "default": [ "RESIDENTIAL" ], "items": { "type": "string" } }, "concurrency": { "title": "Per-business concurrency", "minimum": 1, "maximum": 8, "type": "integer", "description": "Number of businesses processed in parallel. Each business triggers one upstream actor call; raise carefully.", "default": 4 } } }, "runsResponseSchema": { "type": "object", "properties": { "data": { "type": "object", "properties": { "id": { "type": "string" }, "actId": { "type": "string" }, "userId": { "type": "string" }, "startedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "finishedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "status": { "type": "string", "example": "READY" }, "meta": { "type": "object", "properties": { "origin": { "type": "string", "example": "API" }, "userAgent": { "type": "string" } } }, "stats": { "type": "object", "properties": { "inputBodyLen": { "type": "integer", "example": 2000 }, "rebootCount": { "type": "integer", "example": 0 }, "restartCount": { "type": "integer", "example": 0 }, "resurrectCount": { "type": "integer", "example": 0 }, "computeUnits": { "type": "integer", "example": 0 } } }, "options": { "type": "object", "properties": { "build": { "type": "string", "example": "latest" }, "timeoutSecs": { "type": "integer", "example": 300 }, "memoryMbytes": { "type": "integer", "example": 1024 }, "diskMbytes": { "type": "integer", "example": 2048 } } }, "buildId": { "type": "string" }, "defaultKeyValueStoreId": { "type": "string" }, "defaultDatasetId": { "type": "string" }, "defaultRequestQueueId": { "type": "string" }, "buildNumber": { "type": "string", "example": "1.0.0" }, "containerUrl": { "type": "string" }, "usage": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "integer", "example": 1 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } }, "usageTotalUsd": { "type": "number", "example": 0.00005 }, "usageUsd": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "number", "example": 0.00005 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } } } } } } } } } ```