# 💬 Instagram Comments Scraper Pro (`ayeeyee/instagram-comments-scraper-pro`) Actor Instagram comment intelligence with sentiment analysis, spam detection, and engagement scoring. Desktop+embed fallback chain. MCP-ready metadata. 3 modes. - **URL**: https://apify.com/ayeeyee/instagram-comments-scraper-pro.md - **Developed by:** [Virtual Footprint LLC](https://apify.com/ayeeyee) (community) - **Categories:** Social media, Marketing - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing from $1.50 / 1,000 results 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 web data automations that power AI and operations. They run on the Apify platform to scrape websites, process data, connect APIs, and automate workflows. 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/docs.md): ```bash npm install apify-client ``` In Python projects, use official [Python client library](https://docs.apify.com/api/client/python/docs.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/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 ## Instagram Comments Scraper Pro ![Apify](https://img.shields.io/badge/Apify-Actor-ff6b37?logo=apify) ![Version](https://img.shields.io/badge/version-3.0-blue) ![Pricing](https://img.shields.io/badge/pricing-PAY_PER_EVENT-success) ![Platform](https://img.shields.io/badge/platform-Instagram-E4405F?logo=instagram) ![Modes](https://img.shields.io/badge/modes-fast_lookup%7Cenrich%7Cbatch-orange) ![MCP-ready](https://img.shields.io/badge/MCP-ready-brightgreen) > Comment intelligence from Instagram posts: sentiment analysis, spam detection, engagement scoring, and MCP-ready `providerHealth` metadata. Desktop+embed fallback chain. No login cookies required. --- ### Why This Actor Is Better #### Competitor comparison | Feature | **This Actor** | Apify IG Comments (top) | Free IG scrapers | Sentiment SaaS | |---|:---:|:---:|:---:|:---:| | No login cookies required | ✅ | ❌ | ❌ | n/a | | Desktop+embed fallback | ✅ | ❌ | ❌ | n/a | | Keyword sentiment | ✅ open-source | ❌ | ❌ | ✅ paid | | Spam detection | ✅ heuristics | ❌ | ❌ | ✅ paid | | Engagement labels | ✅ | ❌ | ❌ | partial | | Optional OpenAI NLP | ✅ user key | ❌ | ❌ | ✅ paid | | Confidence score (0-1) | ✅ | ❌ | ❌ | ❌ | | MCP-ready metadata | ✅ `providerHealth` | ❌ | ❌ | ❌ | | Price / 1K comments | **$1.77** | ~$2.50 | free (rate-limited) | ~$5-20 | #### Key Features - 🛡️ **Multi-API fallback chain** — Instagram desktop (Playwright) primary with automatic embed/oembed HTML fallback. - 💬 **Sentiment analysis** — open-source keyword classifier (positive/negative/neutral) on every comment. - 🚫 **Spam detection** — heuristic scoring flags spam/suspicious/clean comments (URLs, keywords, ALL CAPS). - 📊 **Engagement labels** — `high`/`medium`/`low`/`none` based on likes. - 🎯 **Confidence scoring** — 0.0–1.0 reliability score on every result. - 🔗 **Source attribution** — know which providers contributed each field. - ⚡ **Cache-first mode** — `fast_lookup` hits KVS cache (30min TTL — comments change fast). - 🤖 **MCP-ready** — `providerHealth{}` on every result for agent routing. - 🔌 **Optional OpenAI NLP** — drop in `OPENAI_API_KEY` for GPT-4o-mini sentiment. Disabled by default. - 💰 **Transparent PPE pricing** — pay only for successful comments. --- ### Architecture ```mermaid flowchart TD A[Input: post URLs + mode] --> B{Cache hit?} B -- yes --> C[Return cached base comments] B -- no --> D[Primary: Instagram desktop Playwright] D -- fails --> E[Fallback: Instagram embed HTML httpx] D --> F[Normalize: author/text/likes/timestamp] E --> F F --> G[Enrichment layer] G --> G1[Keyword sentiment] G --> G2[Spam detection heuristics] G --> G3[Email/phone/URL extraction] G --> G4[Engagement labels] G --> G5[Optional: OpenAI NLP sentiment] G1 --> H[Confidence scoring + source attribution] G2 --> H G3 --> H G4 --> H G5 --> H H --> I[Progressive dataset push] I --> J[Webhook + MCP-ready metadata] C --> J ```` *** ### Modes | Mode | Description | Target latency | Use case | |---|---|---|---| | `fast_lookup` | Cache-first, base comments only | <800ms cached | Quick counts, dedup | | `enrich` | Sentiment + spam + engagement + optional NLP | ~2-4s/post | Brand monitoring, audience analysis | | `batch` | Queue-based, full enrichment, per-item isolation | varies | Large post lists (100+) | *** ### Input | Parameter | Type | Required | Default | Description | |---|---|:---:|---|---| | `mode` | string | — | `enrich` | `fast_lookup` | `enrich` | `batch` | | `queries` | array | ✅ | `["https://instagram.com/p/Cxxx"]` | Instagram post URLs | | `maxResults` | integer | — | `25` | Max comments per post (1–1000) | | `webhookUrl` | string | — | — | Webhook for completion notification | #### Example input ```json { "mode": "enrich", "queries": ["https://www.instagram.com/p/Cxxx/", "https://www.instagram.com/p/Cyyy/"], "maxResults": 100, "webhookUrl": "https://your-app.com/webhook" } ``` *** ### Output | Field | Type | Description | |---|---|---| | `query` | string | Input query | | `postUrl` | string | Source post URL | | `author` | string | Commenter handle | | `text` | string | Comment text | | `timestamp` | string | Comment timestamp | | `likes` | integer | Like count | | `engagementLabel` | string | `high` | `medium` | `low` | `none` | | `sentiment` | string | `positive` | `negative` | `neutral` | | `sentimentNlp` | string | null | OpenAI NLP sentiment (if key provided) | | `spamLabel` | string | `clean` | `suspicious` | `spam` | | `spamScore` | number | 0.0–1.0 spam probability | | `emails` | array | Emails found in comment | | `phones` | array | Phones found in comment | | `urls` | array | URLs found in comment | | `confidenceScore` | number | 0.0–1.0 reliability | | `sources` | array | Provider attribution | | `providerHealth` | object | Per-provider status/latency | | `cacheStatus` | string | `hit` | `miss` | `degraded` | | `mode` | string | Execution mode | | `extractedAt` | string | ISO timestamp | #### Example output ```json { "query": "https://www.instagram.com/p/Cxxx/", "postUrl": "https://www.instagram.com/p/Cxxx/", "author": "jane_doe", "text": "Love this! So beautiful 😍", "timestamp": "2026-06-28T10:00:00Z", "likes": 42, "engagementLabel": "medium", "sentiment": "positive", "spamLabel": "clean", "spamScore": 0.0, "emails": [], "phones": [], "urls": [], "confidenceScore": 0.8, "sources": ["instagram", "sentiment_analyzer", "spam_detector"], "providerHealth": { "instagram_desktop": {"status": "ok", "latency_ms": 3200, "error": null}, "sentiment_analyzer": {"status": "ok", "latency_ms": 0, "error": null}, "spam_detector": {"status": "ok", "latency_ms": 0, "error": null} }, "cacheStatus": "miss", "mode": "enrich", "extractedAt": "2026-06-28T23:50:00.000Z" } ``` *** ### Pricing | Plan | Price per 1K comments | Savings vs. top competitor | |---|---|---| | Leading competitors | ~$2.50/1K | — | | **This actor (≤10K/mo)** | **$1.77/1K** | **29% cheaper** | | **This actor (10K–100K/mo)** | **$1.50/1K** | **40% cheaper** | | **This actor (100K+/mo)** | **$1.25/1K** | **50% cheaper** | Optional event: `sentiment_scored` at $0.30/1K comments with sentiment analysis. *** ### Use Cases - **Brand monitoring** — track sentiment across product launch posts - **Audience analysis** — identify engaged commenters and filter spam - **Crisis detection** — spike in negative sentiment alerts PR teams - **Influencer vetting** — measure real engagement vs spam in comment sections - **Lead generation** — extract emails/phones left in comments - **MCP agent workflows** — `providerHealth` lets agents route around failures - **Content research** — find high-engagement comments for testimonials - **Compliance** — flag spam/promotional/policy-violating comments at scale *** ### Integration Examples #### Python (Apify SDK) ```python from apify_client import ApifyClient client = ApifyClient("YOUR_APIFY_TOKEN") run = client.actor("ayeeyee/instagram-comments-scraper-pro").call(run_input={ "mode": "enrich", "queries": ["https://www.instagram.com/p/Cxxx/"], "maxResults": 100, }) for item in client.dataset(run["defaultDatasetId"]).iterate_items(): print(f"@{item['author']}: {item['sentiment']} | spam={item['spamLabel']} | {item['text'][:50]}") ``` #### cURL ```bash curl -X POST "https://api.apify.com/v2/acts/ayeeyee~instagram-comments-scraper-pro/runs?token=YOUR_TOKEN" \ -H "Content-Type: application/json" \ -d '{"mode":"enrich","queries":["https://www.instagram.com/p/Cxxx/"],"maxResults":50}' ``` #### MCP (Model Context Protocol) ```bash npx -y @apify/actors-mcp-server --tools actors,ayeeyee/instagram-comments-scraper-pro ``` Agents can call `call-actor` and use `providerHealth` + `spamLabel` + `sentiment` to filter and route. *** ### FAQ **Q: Do I need Instagram login cookies?** No. Public post comments are scraped via DOM selectors and meta tags — no login required. Private posts return no comments. **Q: How accurate is the sentiment?** The default keyword classifier is ~70-80% accurate for obvious positive/negative comments. For higher accuracy, set `OPENAI_API_KEY` to enable GPT-4o-mini NLP sentiment. **Q: What does the spam detector flag?** Comments with spam keywords ("dm me", "free follower", "buy now"), multiple URLs, all-caps shouting, or very short comments with links. Score 0.5+ = spam, 0.3-0.5 = suspicious, <0.3 = clean. **Q: Can I call this from an LLM agent?** Yes. MCP-ready with `providerHealth{}`, `sentiment`, `spamLabel`, and `confidenceScore` for routing/filtering decisions. **Q: What's the cache TTL?** 30 minutes — comments change faster than profiles or business listings. *** ### Legal & Compliance Scrapes **publicly available** Instagram comment data. Does not access private data, bypass authentication, or store credentials. Users are responsible for complying with GDPR/CCPA and Instagram's ToS. *** ### AI-DLC / Data Lifecycle - **Collection** — Public data only; respects robots.txt and rate limits. - **Processing** — In-memory normalization; no PII logging. - **Storage** — Results in user's Apify dataset, not retained by actor. - **Usage** — Brand monitoring, audience analysis, legitimate research. - **Disposal** — No long-term caching (30min TTL for base results only). *** ### Enhancement Roadmap (API / MCP Integrations) - **OpenAI sentiment MCP** — GPT-4o-mini NLP sentiment (optional, user key) - **Google Perspective API MCP** — toxicity scoring - **LangGraph workflow** — IG comments → sentiment aggregation → alert routing - **Vector store** — semantic comment clustering across posts *** ### Changelog - **v3.0** — Multi-API orchestration edition: desktop+embed fallback, spam detection, engagement labels, MCP-ready `providerHealth`, optional OpenAI NLP, expanded FAQ, integration examples, volume pricing. - **v2.0** — Premium README, AI-DLC docs, confidence scoring, source attribution. - **v1.0** — Initial release with Playwright scraping and keyword sentiment. *** ### Links - **Apify Store:** https://apify.com/ayeeyee/instagram-comments-scraper-pro - **Actor ID:** `Jgfr5cR4ctbyPbX79` # Actor input Schema ## `mode` (type: `string`): fast\_lookup: cache-first base comments. enrich: sentiment + spam + engagement + optional OpenAI NLP. batch: queue-based with per-item isolation. ## `queries` (type: `array`): List of Instagram post URLs to extract comments from. ## `maxResults` (type: `integer`): Maximum number of comments to return per post (1-1000). ## `webhookUrl` (type: `string`): Optional URL to receive a POST notification when the actor run completes. ## Actor input object example ```json { "mode": "enrich", "queries": [ "https://instagram.com/p/Cxxx" ], "maxResults": 25, "webhookUrl": "" } ``` # Actor output Schema ## `results` (type: `string`): No description # 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 = {}; // Run the Actor and wait for it to finish const run = await client.actor("ayeeyee/instagram-comments-scraper-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 = {} # Run the Actor and wait for it to finish run = client.actor("ayeeyee/instagram-comments-scraper-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 '{}' | apify call ayeeyee/instagram-comments-scraper-pro --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=ayeeyee/instagram-comments-scraper-pro", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "💬 Instagram Comments Scraper Pro", "description": "Instagram comment intelligence with sentiment analysis, spam detection, and engagement scoring. Desktop+embed fallback chain. MCP-ready metadata. 3 modes.", "version": "3.0", "x-build-id": "BLYe8Wuel8nOUpbmI" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/ayeeyee~instagram-comments-scraper-pro/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-ayeeyee-instagram-comments-scraper-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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/ayeeyee~instagram-comments-scraper-pro/runs": { "post": { "operationId": "runs-sync-ayeeyee-instagram-comments-scraper-pro", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/ayeeyee~instagram-comments-scraper-pro/run-sync": { "post": { "operationId": "run-sync-ayeeyee-instagram-comments-scraper-pro", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": [ "queries" ], "properties": { "mode": { "title": "Execution Mode", "enum": [ "fast_lookup", "enrich", "batch" ], "type": "string", "description": "fast_lookup: cache-first base comments. enrich: sentiment + spam + engagement + optional OpenAI NLP. batch: queue-based with per-item isolation.", "default": "enrich" }, "queries": { "title": "Post URLs", "type": "array", "description": "List of Instagram post URLs to extract comments from.", "default": [ "https://instagram.com/p/Cxxx" ] }, "maxResults": { "title": "Max Comments per Post", "minimum": 1, "maximum": 1000, "type": "integer", "description": "Maximum number of comments to return per post (1-1000).", "default": 25 }, "webhookUrl": { "title": "Webhook URL", "type": "string", "description": "Optional URL to receive a POST notification when the actor run completes.", "default": "" } } }, "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 } } } } } } } } } } ```