# Reddit Brand Sentiment Scraper | Complaints & Reviews (`zen-studio/reddit-brand-sentiment-analyzer`) Actor Extract brand sentiment, complaints, and alternatives from Reddit discussions. Enter any brand name, get structured analysis with real quotes and source URLs. Competitive mode compares multiple brands side by side. Full markdown report for LLM pipelines. - **URL**: https://apify.com/zen-studio/reddit-brand-sentiment-analyzer.md - **Developed by:** [Zen Studio](https://apify.com/zen-studio) (community) - **Categories:** Lead generation, Social media, Automation - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, NaN bookmarks - **User rating**: No ratings yet ## Pricing from $0.10 / quick sentiment report 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 ## Reddit Brand Sentiment Analyzer | What People Really Think (2026) **Find out what Reddit actually thinks about any brand.** Structured sentiment, real quotes with source links, competitor comparisons, and community discovery. From Fortune 500 to niche SaaS. ![Demo](https://iili.io/BxtQeAg.gif)
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Brand Sentiment 		 Reddit Reviews
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Ratings, pros/cons 		 TrustRadius
Enterprise reviews
#### Copy to your AI assistant Copy this block into ChatGPT, Claude, Cursor, or any LLM to start building with this data. ```` Reddit Brand Sentiment Analyzer (zen-studio/reddit-brand-sentiment-analyzer) on Apify. Analyzes any brand's Reddit reputation. Returns: overall sentiment summary, top complaints, recommended alternatives with switching reasons, real quotes with Reddit URLs, discovered communities (subreddits). Input: brand (string, required), competitors (string array, optional), depth ("quick" $0.10 or "full" $0.25, default "full"). Output: JSON with sentiment/complaints/alternatives sections, allQuotes array, communities array, combined markdown report. Competitive mode adds competitors object with full analysis per competitor. Pricing: $0.10 quick / $0.25 full per brand (competitors charged separately). Apify token required. Get token: https://console.apify.com/account/integrations ```` ### Key Features - **Multi-angle analysis** — sentiment, complaints, and alternatives in a single run. Each angle queries Reddit independently for complete coverage. - **Real quotes with sources** — every insight backed by actual Reddit comments with direct URLs. Not generic sentiment scores. - **Competitive mode** — add competitors for side-by-side comparison. See switching patterns and cross-references. - **Community discovery** — find which subreddits discuss your brand and how large those communities are. - **Markdown output** — combined report ready for LLM pipelines, N8N workflows, or feeding into GPT/Claude for further analysis. ### How to Analyze Brand Sentiment on Reddit #### Single brand ```json { "brand": "Notion" } ```` #### Quick snapshot (faster, cheaper) ```json { "brand": "Datadog", "depth": "quick" } ``` #### Competitive analysis ```json { "brand": "Notion", "competitors": ["Obsidian", "Coda"] } ``` ### Input Parameters | Parameter | Type | Default | Description | |-----------|------|---------|-------------| | `brand` | string | *required* | Brand, product, or company to analyze | | `competitors` | string\[] | — | Competitors for side-by-side comparison | | `depth` | select | `full` | `quick` (1 angle, ~15s) or `full` (3 angles, ~30-60s) | ### What Data Can You Extract About a Brand? Every report includes: - **Sentiment** — AI-synthesized summary of how Reddit discusses the brand, with supporting quotes - **Complaints** — most common frustrations and criticisms, sourced from real discussions - **Alternatives** — what people recommend instead and why they switch - **Quotes** — deduplicated across all angles, each with subreddit attribution and direct URL - **Communities** — subreddits where the brand is discussed - **Source posts** — cited Reddit threads with metadata - **Markdown report** — combined narrative ready for LLM consumption #### Output Example ```json { "brand": "Datadog", "canonicalName": "Datadog", "description": "Datadog is a cloud monitoring and analytics platform...", "category": "SaaS / DevOps & Monitoring", "timestamp": "2026-04-04T07:32:22.392818+00:00", "sentiment": { "summary": "People on Reddit have varied opinions about Datadog...", "quotes": [ { "text": "Product is S tier but apparently the most expensive option in the observability space.", "subreddit": "techsales", "url": "https://www.reddit.com/r/techsales/comments/1muiyr1/comment/n9j9uq4/", "sourceType": "comment" } ], "sourceCount": 18 }, "complaints": { "summary": "Datadog's pricing model is the most common frustration...", "quotes": [ { "text": "Datadog is bankruptcy as a service.", "subreddit": "devops", "url": "https://www.reddit.com/r/devops/comments/...", "sourceType": "comment" } ], "sourceCount": 18 }, "alternatives": { "summary": "High cost has led many users to seek alternatives...", "quotes": [ { "text": "We just rolled out SigNoz at my startup to help centralize logging.", "subreddit": "devops", "url": "https://www.reddit.com/r/devops/comments/...", "sourceType": "comment" } ], "sourceCount": 16 }, "allQuotes": [ // 34 deduplicated quotes across all angles ], "communities": [ { "name": "r/devops" }, { "name": "r/sysadmin" }, { "name": "r/techsales" }, { "name": "r/producthunters" } ], "sourcePosts": [ // 27 unique Reddit threads cited ], "followUpQueries": [], "markdown": "## Brand Sentiment: Datadog\n\n### Overall Sentiment\n\n..." } ``` In competitive mode, the output includes a `competitors` object with a full report per competitor: ```json { "brand": "Notion", "sentiment": { ... }, "complaints": { ... }, "alternatives": { ... }, "competitors": { "Obsidian": { "brand": "Obsidian", "sentiment": { ... }, "complaints": { ... }, "alternatives": { ... }, "allQuotes": [ ... ] } } } ``` ### Advanced Usage #### Competitive research pipeline Run multiple competitors to map the competitive landscape. Each brand gets the same full analysis. ```json { "brand": "Slack", "competitors": ["Microsoft Teams", "Discord"], "depth": "full" } ``` The alternatives angle for each brand naturally cross-references the others. If people switch from Slack to Discord, that shows up in Slack's alternatives section. #### Scheduled brand monitoring Schedule this actor to run weekly or monthly. Each run produces a timestamped report. Compare reports over time to track sentiment shifts after product launches, pricing changes, or PR incidents. ```json { "brand": "Robinhood", "depth": "quick" } ``` Use quick mode for scheduled runs to keep costs down ($0.10 vs $0.25 per brand). #### Feed into LLM pipelines The `markdown` field is a complete narrative report ready for GPT, Claude, or any LLM. Use it as context for generating executive summaries, blog posts, or investor briefings. ```python from apify_client import ApifyClient client = ApifyClient("your_token") run = client.actor("zen-studio/reddit-brand-sentiment-analyzer").call( run_input={"brand": "Linear", "depth": "full"} ) for item in client.dataset(run["defaultDatasetId"]).iterate_items(): ## Feed markdown into your LLM llm_context = item["markdown"] ## Or use structured data top_complaints = item["complaints"]["quotes"][:5] ``` #### Customer discovery for founders Find out what people complain about in your space. The complaints and alternatives angles reveal pain points and unmet needs. ```json { "brand": "Monday.com", "competitors": ["Asana", "ClickUp"], "depth": "full" } ``` ### Pricing — Pay Per Event (PPE) | Depth | Per brand | What you get | |-------|-----------|-------------| | **Quick snapshot** | $0.10 | 1 angle (sentiment only), ~15 seconds | | **Full report** | $0.25 | 3 angles (sentiment + complaints + alternatives), ~30-60 seconds | Each brand counts as one event. Competitors are charged separately at the same rate. | Scenario | Cost | |----------|------| | Single brand, quick | $0.10 | | Single brand, full | $0.25 | | Brand + 2 competitors, full | $0.75 | | Brand + 4 competitors, quick | $0.50 | ### What Brands Work? Tested across 14 brands in different categories. Works for any brand, product, or company discussed on Reddit. | Category | Example | Quotes found | |----------|---------|-------------| | SaaS | Notion, Linear, Monday.com | 28-79 | | Consumer | Tesla, SHEIN, Temu | 36-45 | | Fintech | Robinhood | 42 | | B2B | Datadog | 37 | | DTC | Huel, Oatly | 40 | | Dev Tools | Raycast, Cursor | 36-39 | | VPN | NordVPN | 35 | | Niche SaaS | GorillaDesk (pest control) | 32 | | Medical | Cala (wearable) | 32 | Niche brands with limited Reddit presence still produce useful reports. GorillaDesk returned 32 quotes from r/PestControlIndustry. ### How It Works 1. **Identify** — resolves brand name to its canonical form, detects generic names (e.g. "Linear", "Monday") to avoid confusion with common words 2. **Analyze** — fires 3 parallel queries to Reddit's AI synthesis engine, each targeting a different angle (sentiment, complaints, alternatives) 3. **Merge** — deduplicates quotes and communities across angles, builds a unified report with combined markdown Full mode takes 30-60 seconds. Quick mode takes 15-20 seconds. ### FAQ **What brands work?** Any brand, product, or company discussed on Reddit. Works across all categories: SaaS, consumer products, fintech, health, automotive, dev tools. **What if a brand has a generic name like "Linear" or "Monday"?** The actor automatically detects generic names and adds context to prevent confusion. "Linear" returns results about the project management tool, not linear algebra. **How fresh is the data?** Every run queries Reddit live. There is no cache. Results reflect current discussions. **What's the difference between quick and full mode?** Quick runs 1 angle (sentiment only), full runs 3 (sentiment + complaints + alternatives). Both cost the same per brand, but quick is faster. **How does competitive mode work?** Add competitor names to the `competitors` field. Each competitor gets the same full analysis. The alternatives angle for each brand naturally references the others, revealing switching patterns. **Can I schedule recurring runs?** Yes. Use Apify's built-in scheduler to run weekly or monthly. Compare timestamped reports to track sentiment shifts over time. **What if there are no Reddit discussions about my brand?** You'll get a report with empty or minimal data. The actor only returns what Reddit actually contains. **How are quotes selected?** Quotes are extracted from Reddit's AI-synthesized answers, which pull from thousands of discussions. Each quote includes the source subreddit and a direct URL to the original comment. **Can I use this for content marketing?** Yes. The quotes and pain points make excellent source material for blog posts, social media, and thought leadership content. Every quote has attribution. ### Support - **Bugs**: Issues tab - **Features**: Issues tab ### Legal Compliance Extracts publicly available data from Reddit. Users must comply with Reddit's terms of service and applicable data protection regulations (GDPR, CCPA). *** *Structured brand intelligence from Reddit. Real quotes, real communities, real sentiment.* # Actor input Schema ## `brand` (type: `string`): Brand, product, or company to analyze.

Works with any brand discussed on Reddit. ## `depth` (type: `string`): Quick snapshot — 1 angle (sentiment only), ~15 seconds.
Full report — 3 angles (sentiment + complaints + alternatives), ~30-60 seconds. ## `competitors` (type: `array`): Add competitor brand names for side-by-side comparison.

Each competitor gets the same full analysis as your primary brand. The output reveals switching patterns: which competitors people recommend instead, and why they switch.

Example: Analyzing "Notion" with competitors "Obsidian" and "Coda" shows how Reddit users compare all three.

Each competitor is charged as a separate brand analysis. ## Actor input object example ```json { "brand": "Notion", "depth": "full" } ``` # 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 = { "brand": "Notion" }; // Run the Actor and wait for it to finish const run = await client.actor("zen-studio/reddit-brand-sentiment-analyzer").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 = { "brand": "Notion" } # Run the Actor and wait for it to finish run = client.actor("zen-studio/reddit-brand-sentiment-analyzer").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 '{ "brand": "Notion" }' | apify call zen-studio/reddit-brand-sentiment-analyzer --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=zen-studio/reddit-brand-sentiment-analyzer", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Reddit Brand Sentiment Scraper | Complaints & Reviews", "description": "Extract brand sentiment, complaints, and alternatives from Reddit discussions. Enter any brand name, get structured analysis with real quotes and source URLs. Competitive mode compares multiple brands side by side. Full markdown report for LLM pipelines.", "version": "0.0", "x-build-id": "MQriY9jrgvncvLweQ" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/zen-studio~reddit-brand-sentiment-analyzer/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-zen-studio-reddit-brand-sentiment-analyzer", "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/zen-studio~reddit-brand-sentiment-analyzer/runs": { "post": { "operationId": "runs-sync-zen-studio-reddit-brand-sentiment-analyzer", "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/zen-studio~reddit-brand-sentiment-analyzer/run-sync": { "post": { "operationId": "run-sync-zen-studio-reddit-brand-sentiment-analyzer", "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": [ "brand" ], "properties": { "brand": { "title": "Brand", "type": "string", "description": "Brand, product, or company to analyze.

Works with any brand discussed on Reddit." }, "depth": { "title": "Report depth", "enum": [ "quick", "full" ], "type": "string", "description": "Quick snapshot — 1 angle (sentiment only), ~15 seconds.
Full report — 3 angles (sentiment + complaints + alternatives), ~30-60 seconds.", "default": "full" }, "competitors": { "title": "Competitors", "type": "array", "description": "Add competitor brand names for side-by-side comparison.

Each competitor gets the same full analysis as your primary brand. The output reveals switching patterns: which competitors people recommend instead, and why they switch.

Example: Analyzing \"Notion\" with competitors \"Obsidian\" and \"Coda\" shows how Reddit users compare all three.

Each competitor is charged as a separate brand analysis.", "items": { "type": "string" } } } }, "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 } } } } } } } } } } ```