# HN/Reddit Sentiment Analyzer (`darknezz/sentiment-analyzer`) Actor Fetch posts from Hacker News and Reddit, run VADER sentiment analysis, and output structured results with sentiment scores, keywords, and metadata. Perfect for brand monitoring, trend detection, and market research. - **URL**: https://apify.com/darknezz/sentiment-analyzer.md - **Developed by:** [Oaida Adrian](https://apify.com/darknezz) (community) - **Categories:** AI, Marketing - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing Pay per usage This Actor is paid per platform usage. The Actor is free to use, and you only pay for the Apify platform usage, which gets cheaper the higher subscription plan you have. Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#pay-per-usage ## 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 ## HN/Reddit Sentiment Analyzer Fetches posts from Hacker News and Reddit, runs VADER sentiment analysis, and outputs structured results. ### What It Does - **Hacker News**: Fetches top or new stories via the Firebase API - **Reddit**: Fetches posts from any subreddit (hot, new, top, rising) - **Sentiment Analysis**: VADER scores for positive, negative, neutral, and compound - **Keywords**: Top keywords extracted from each post - **Aggregation**: Overall sentiment breakdown and average compound score ### Input Options | Option | Description | |--------|-------------| | `sources` | `hackernews`, `reddit`, or `both` | | `subreddit` | Comma-separated subreddits (required for reddit) | | `sortBy` | Reddit sort: hot, new, top, rising | | `hnSort` | HN sort: top or new | | `maxPosts` | Max posts to analyse (default: 50) | | `sentimentFilter` | Filter by positive, negative, or neutral | | `minScore` | Minimum upvotes required | ### Output Each dataset item includes: ```json { "source": "hackernews", "title": "Show HN: I built a sentiment analyzer", "sentiment": "positive", "sentimentScores": { "positive": 0.4521, "neutral": 0.5479, "negative": 0.0, "compound": 0.6486 }, "keywords": ["sentiment", "analyzer", "built"], "score": 125, "commentsCount": 42 } ```` ### Use Cases - **Brand monitoring**: Track sentiment about your product on HN and Reddit - **Trend detection**: Spot negative sentiment before it goes viral - **Market research**: Understand community feeling about technologies - **Content curation**: Filter positive or negative discussions # Actor input Schema ## `sources` (type: `string`): Data sources to fetch from. ## `subreddit` (type: `string`): Comma-separated subreddit names (e.g. technology, programming). Required if sources includes reddit. ## `sortBy` (type: `string`): Sort order for Reddit posts. ## `hnSort` (type: `string`): Sort order for Hacker News stories. ## `maxPosts` (type: `integer`): Maximum number of posts to analyse. ## `sentimentFilter` (type: `string`): Only return posts matching this sentiment. ## `minScore` (type: `integer`): Only return posts with at least this many upvotes. ## `proxyConfiguration` (type: `object`): Use Apify proxy to avoid rate limits. ## Actor input object example ```json { "sources": "hackernews", "subreddit": "", "sortBy": "hot", "hnSort": "top", "maxPosts": 50, "sentimentFilter": "", "minScore": 0, "proxyConfiguration": { "useApifyProxy": true } } ``` # 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 = { "proxyConfiguration": { "useApifyProxy": true } }; // Run the Actor and wait for it to finish const run = await client.actor("darknezz/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 = { "proxyConfiguration": { "useApifyProxy": True } } # Run the Actor and wait for it to finish run = client.actor("darknezz/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 '{ "proxyConfiguration": { "useApifyProxy": true } }' | apify call darknezz/sentiment-analyzer --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=darknezz/sentiment-analyzer", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "HN/Reddit Sentiment Analyzer", "description": "Fetch posts from Hacker News and Reddit, run VADER sentiment analysis, and output structured results with sentiment scores, keywords, and metadata. Perfect for brand monitoring, trend detection, and market research.", "version": "0.2", "x-build-id": "pPrvZaXCP94sV9wAD" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/darknezz~sentiment-analyzer/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-darknezz-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/darknezz~sentiment-analyzer/runs": { "post": { "operationId": "runs-sync-darknezz-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/darknezz~sentiment-analyzer/run-sync": { "post": { "operationId": "run-sync-darknezz-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", "properties": { "sources": { "title": "Sources", "enum": [ "hackernews", "reddit", "both" ], "type": "string", "description": "Data sources to fetch from.", "default": "hackernews" }, "subreddit": { "title": "Subreddits", "type": "string", "description": "Comma-separated subreddit names (e.g. technology, programming). Required if sources includes reddit.", "default": "" }, "sortBy": { "title": "Reddit Sort", "enum": [ "hot", "new", "top", "rising" ], "type": "string", "description": "Sort order for Reddit posts.", "default": "hot" }, "hnSort": { "title": "HN Sort", "enum": [ "top", "new" ], "type": "string", "description": "Sort order for Hacker News stories.", "default": "top" }, "maxPosts": { "title": "Max Posts", "minimum": 1, "maximum": 500, "type": "integer", "description": "Maximum number of posts to analyse.", "default": 50 }, "sentimentFilter": { "title": "Filter by Sentiment", "enum": [ "", "positive", "negative", "neutral" ], "type": "string", "description": "Only return posts matching this sentiment.", "default": "" }, "minScore": { "title": "Minimum Score", "type": "integer", "description": "Only return posts with at least this many upvotes.", "default": 0 }, "proxyConfiguration": { "title": "Proxy configuration", "type": "object", "description": "Use Apify proxy to avoid rate limits." } } }, "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 } } } } } } } } } } ```