# Reddit Sentiment Analyzer (`echocall/reddit-sentiment-analyzer`) Actor Analyzes the sentiment (positive/negative/neutral) of Reddit posts for any subreddit. - **URL**: https://apify.com/echocall/reddit-sentiment-analyzer.md - **Developed by:** [Kristian Gasic](https://apify.com/echocall) (community) - **Categories:** Automation, Lead generation, Other - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, NaN bookmarks - **User rating**: No ratings yet ## Pricing from $0.10 / 1,000 results This Actor is paid per event and usage. You are charged both the fixed price for specific events and for Apify platform usage. 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 Sentiment Analyzer 🚀📊 **Apify Actor** – Analyzes sentiment of Reddit posts for brand monitoring, market research, and trend analysis. ### What does this Actor do? The **Reddit Sentiment Analyzer** scrapes posts from any subreddit and automatically performs sentiment analysis. Each post is classified as **positive**, **negative**, or **neutral**, enabling quick market sentiment checks and strategic insights. Perfect for: - **Investor sentiment analysis** – Track bullish/bearish sentiment in `r/stocks`, `r/wallstreetbets`, `r/cryptocurrency` - **Brand monitoring** – See what Reddit says about your product or competitor - **Trend detection** – Identify emerging topics and community sentiment shifts - **Market research** – Fast-track consumer feedback at scale ### How it works 1. Fetches posts from your chosen subreddit 2. Uses **Puppeteer + Residential Proxy** to bypass Reddit's IP blocks 3. Falls back to **Reddit's JSON API** if needed (always reliable) 4. Analyzes text sentiment using industry-standard NLP 5. Outputs individual post data + summary statistics ### Input Parameters | Parameter | Type | Required | Default | Description | |---|---|---|---|---| | `subreddit` | string | ✅ | — | Subreddit name (e.g., `stocks`, `wallstreetbets`) | | `query` | string | ❌ | empty | Search term to filter posts (e.g., `Tesla`). Leave empty for all posts. | | `limit` | number | ❌ | 50 | Number of posts to fetch (1–200) | | `sortBy` | enum | ❌ | `new` | How to sort: `hot`, `new`, or `top` | #### Example Input ```json { "subreddit": "stocks", "query": "Tesla", "limit": 50, "sortBy": "new" } ```` ### Output The Actor saves two types of data to the dataset: #### 1. Individual Post Items (one per post) ```json { "id": "abc123", "title": "Is Tesla still a good buy?", "score": 120, "numComments": 45, "url": "https://reddit.com/r/stocks/comments/abc123", "createdUtc": "2026-06-08T12:00:00.000Z", "sentiment": { "score": -3, "comparative": -0.25, "label": "negative", "tokens": ["bad", "worried", "overpriced"] }, "overallSentiment": "Negative sentiment (score: -3) with keywords: bad, worried, overpriced" } ``` #### 2. Summary Item (final analysis) ```json { "type": "summary", "subreddit": "stocks", "query": "Tesla", "sortBy": "new", "totalPostsAnalyzed": 50, "averageSentiment": 1.2, "positiveCount": 18, "negativeCount": 12, "neutralCount": 20, "topPositivePost": {"title": "...", "url": "..."}, "topNegativePost": {"title": "...", "url": "..."} } ``` ### Important: Residential Proxy Requirement ⚠️ **This Actor uses Apify Residential Proxies** – Reddit blocks all datacenter IPs. - Real ISP IPs guarantee access (no 403 errors) - Essential for reliable automation - Factor proxy costs into your usage plan - Runs only in Apify cloud (not locally without proxy setup) ### Limits & Fair Use - ✅ **Max 200 posts** per run - ✅ Works with **public & restricted subreddits** - ✅ Gracefully handles errors (404, private communities, empty subreddits) - ✅ Sentiment analysis optimized for **English text** - ✅ Respects Reddit's terms of service (no authentication bypass, no rate limiting) ### Local Testing ```bash ## Install dependencies npm install ## Start a local Apify run apify run ``` You will be prompted for a `subreddit` name (input from `INPUT_SCHEMA.json`). ### Deployment to Apify ```bash ## Push the Actor to the Apify Console apify push ``` Then in the **Apify Console**: 1. Navigate to your Actor. 2. Click **"Publish to Store"**. 3. Choose **"Pay-per-event"** as monetization. 4. Suggested price: **$1.99 per 100 analyzed posts**. ### Tech Stack - **[Crawlee](https://crawlee.dev/)** – `CheerioCrawler` for HTTP requests and scraping - **[sentiment](https://www.npmjs.com/package/sentiment)** – AFINN-based sentiment analysis for Node.js - **[Apify SDK](https://docs.apify.com/sdk/js/)** – Dataset management & Actor runtime - Optional: **[franc](https://www.npmjs.com/package/franc)** – Language detection to filter non-English posts ### License MIT # Actor input Schema ## `subreddit` (type: `string`): Name of the subreddit to analyze, e.g. 'stocks', 'wallstreetbets', 'cryptocurrency'. ## `query` (type: `string`): Optional search term to filter posts, e.g. 'Tesla'. If empty, the newest/latest posts are fetched. ## `limit` (type: `integer`): Maximum number of posts to analyze (default: 50, max: 200). ## `sortBy` (type: `string`): How to sort the Reddit posts. ## Actor input object example ```json { "subreddit": "stocks", "limit": 50, "sortBy": "new" } ``` # API You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup. ## JavaScript example ```javascript import { ApifyClient } from 'apify-client'; // Initialize the ApifyClient with your Apify API token // Replace the '' with your token const client = new ApifyClient({ token: '', }); // Prepare Actor input const input = { "subreddit": "stocks", "query": "" }; // Run the Actor and wait for it to finish const run = await client.actor("echocall/reddit-sentiment-analyzer").call(input); // Fetch and print Actor results from the run's dataset (if any) console.log('Results from dataset'); console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${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 = { "subreddit": "stocks", "query": "", } # Run the Actor and wait for it to finish run = client.actor("echocall/reddit-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 '{ "subreddit": "stocks", "query": "" }' | apify call echocall/reddit-sentiment-analyzer --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=echocall/reddit-sentiment-analyzer", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Reddit Sentiment Analyzer", "description": "Analyzes the sentiment (positive/negative/neutral) of Reddit posts for any subreddit.", "version": "1.0", "x-build-id": "Ls1veYcqolcrWQnNh" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/echocall~reddit-sentiment-analyzer/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-echocall-reddit-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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/echocall~reddit-sentiment-analyzer/runs": { "post": { "operationId": "runs-sync-echocall-reddit-sentiment-analyzer", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/echocall~reddit-sentiment-analyzer/run-sync": { "post": { "operationId": "run-sync-echocall-reddit-sentiment-analyzer", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": [ "subreddit" ], "properties": { "subreddit": { "title": "Subreddit", "type": "string", "description": "Name of the subreddit to analyze, e.g. 'stocks', 'wallstreetbets', 'cryptocurrency'." }, "query": { "title": "Search query", "type": "string", "description": "Optional search term to filter posts, e.g. 'Tesla'. If empty, the newest/latest posts are fetched." }, "limit": { "title": "Post limit", "minimum": 1, "maximum": 200, "type": "integer", "description": "Maximum number of posts to analyze (default: 50, max: 200).", "default": 50 }, "sortBy": { "title": "Sort by", "enum": [ "hot", "new", "top" ], "type": "string", "description": "How to sort the Reddit posts.", "default": "new" } } }, "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 } } } } } } } } } } ```