# Tomato Cart Reddit Search AI Sentiment Analyzer 100/mo free (`tomato_cart/reddit-multi-search`) Actor Tomato Cart - Multi-keyword Reddit search + sentiment analysis (VADER). Auto-deduplication. First 100 results free, then $2.99/1K. No login, pay as you go. More features than alternatives, lower price. - **URL**: https://apify.com/tomato\_cart/reddit-multi-search.md - **Developed by:** [Richard](https://apify.com/tomato_cart) (community) - **Categories:** Social media, E-commerce, Automation - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing from $2.99 / 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 Multi-Keyword Search + Sentiment **Search Reddit with multiple keywords, auto-deduplicate, and get VADER sentiment scores — no login, no API key, no browser.** ### Why This Actor? - **Multi-keyword batch search** — Enter multiple keywords, search concurrently, results merged and deduplicated by post ID - **Built-in sentiment analysis** — VADER sentiment score (-1 to 1) on every post. No external API, no extra cost - **No login required** — Uses Reddit's public .json endpoints. No browser, no cookies, no API keys - **Fast and lightweight** — Pure HTTP, ~50 posts in 2 seconds. Runs on 256MB memory - **Cheaper than alternatives** — $2.99/1K vs $3.40/1K (trudax Reddit Scraper Lite). First 100 results free per run ### Quick Start #### 1. Enter Keywords Type one keyword per line: ```` python machine learning data science ```` #### 2. Configure (optional) | Field | Default | Description | |-------|---------|-------------| | Posts Per Keyword | 25 | Max results per keyword | | Sort | relevance | relevance / hot / new / top | | Sentiment | ON | VADER score for each post | | Time Range | all | hour / day / week / month / year | #### 3. Run Actor pushes results to your Apify dataset. Download as JSON, CSV, Excel, or XML. ### Output Example ```json { "id": "1px246m", "title": "Is Rust the future?", "score": 362, "num_comments": 314, "sentiment": 0.9796, "keyword": "rust", "url": "https://www.reddit.com/r/rust/...", "permalink": "/r/rust/comments/...", "created_utc": 1766853515.0, "subreddit": "rust" } ```` #### Output Fields | Field | Type | Description | |-------|------|-------------| | id | string | Unique Reddit post ID | | title | string | Post title | | score | int | Upvote count | | num\_comments | int | Comment count | | sentiment | float | VADER compound (-1 to 1) or null if disabled | | keyword | string | Which keyword matched this post | | url | string | Full post URL | | permalink | string | Reddit permalink path | | created\_utc | float | Unix timestamp | | subreddit | string | Subreddit name | ### Config Reference | Input Field | Required | Default | Description | |-------------|----------|---------|-------------| | keywords | Yes | - | Array of search keywords | | maxResultsPerKeyword | No | 25 | Posts per keyword (1-100) | | sort | No | relevance | relevance / hot / new / top | | enableSentiment | No | true | Toggle VADER sentiment | | timeFilter | No | all | hour / day / week / month / year | ### Pricing - $2.99 per 1,000 results (Pay Per Result) - **First 100 results free per run** — try before you buy - No hidden platform usage charges - Proxy usage billed separately by Apify if residential proxy is auto-enabled on block ### FAQ **Q: Do I need to log in to Reddit?** A: No. This Actor uses Reddit's public .json endpoints. No login, no API key required. **Q: How accurate is the sentiment analysis?** A: VADER is a lexicon and rule-based sentiment analysis tool optimized for social media. It works well on Reddit post titles and text. Runs entirely locally with no external API calls. **Q: Does it support non-English keywords?** A: Yes. Reddit's search API accepts any Unicode text. However VADER sentiment is English-only. **Q: What happens if Reddit blocks my IP?** A: The Actor auto-detects blocks (HTTP 403) and falls back to Apify residential proxy. Enable this in your Apify account. **Q: How is this better than trudax/reddit-scraper-lite?** A: Cheaper ($2.99 vs $3.40/1K), supports batch multi-keyword search with deduplication, and includes built-in sentiment analysis. ### Tech - Runtime: Python 3.12 - Memory: 256MB (minimal) - HTTP: requests - Sentiment: VADER (vaderSentiment) - Data source: Reddit public .json endpoints # Actor input Schema ## `keywords` (type: `array`): Search keywords. Multiple keywords are searched concurrently and deduplicated by post ID. ## `maxResultsPerKeyword` (type: `integer`): Maximum number of posts to fetch per keyword. Total raw results = keywords x this value, then deduplicated. ## `sort` (type: `string`): Sort order for search results. ## `enableSentiment` (type: `boolean`): Add VADER sentiment score (-1 to 1) to each post. Runs locally, no external API calls. ## `timeFilter` (type: `string`): Time filter for search results. Only applies when sort is top or new. ## `proxyConfiguration` (type: `object`): Optional proxy. Not needed for local runs. Enable if Reddit blocks your datacenter IP. ## Actor input object example ```json { "keywords": [ "python", "ai", "coding" ], "maxResultsPerKeyword": 25, "sort": "relevance", "enableSentiment": true, "timeFilter": "all", "proxyConfiguration": { "useApifyProxy": false } } ``` # 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 = { "keywords": [ "python", "ai", "coding" ], "proxyConfiguration": { "useApifyProxy": false } }; // Run the Actor and wait for it to finish const run = await client.actor("tomato_cart/reddit-multi-search").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 = { "keywords": [ "python", "ai", "coding", ], "proxyConfiguration": { "useApifyProxy": False }, } # Run the Actor and wait for it to finish run = client.actor("tomato_cart/reddit-multi-search").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 '{ "keywords": [ "python", "ai", "coding" ], "proxyConfiguration": { "useApifyProxy": false } }' | apify call tomato_cart/reddit-multi-search --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=tomato_cart/reddit-multi-search", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Tomato Cart Reddit Search AI Sentiment Analyzer 100/mo free", "description": "Tomato Cart - Multi-keyword Reddit search + sentiment analysis (VADER). Auto-deduplication. First 100 results free, then $2.99/1K. No login, pay as you go. More features than alternatives, lower price.", "version": "0.1", "x-build-id": "4AJljHyv7hrQ3OfqJ" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/tomato_cart~reddit-multi-search/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-tomato_cart-reddit-multi-search", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for its completion, and returns Actor's dataset items in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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/tomato_cart~reddit-multi-search/runs": { "post": { "operationId": "runs-sync-tomato_cart-reddit-multi-search", "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/tomato_cart~reddit-multi-search/run-sync": { "post": { "operationId": "run-sync-tomato_cart-reddit-multi-search", "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": [ "keywords" ], "properties": { "keywords": { "title": "Keywords", "type": "array", "description": "Search keywords. Multiple keywords are searched concurrently and deduplicated by post ID.", "items": { "type": "string" } }, "maxResultsPerKeyword": { "title": "Posts Per Keyword", "minimum": 1, "maximum": 100, "type": "integer", "description": "Maximum number of posts to fetch per keyword. Total raw results = keywords x this value, then deduplicated.", "default": 25 }, "sort": { "title": "Sort Order", "enum": [ "relevance", "hot", "new", "top" ], "type": "string", "description": "Sort order for search results.", "default": "relevance" }, "enableSentiment": { "title": "Sentiment Analysis", "type": "boolean", "description": "Add VADER sentiment score (-1 to 1) to each post. Runs locally, no external API calls.", "default": true }, "timeFilter": { "title": "Time Range", "enum": [ "all", "hour", "day", "week", "month", "year" ], "type": "string", "description": "Time filter for search results. Only applies when sort is top or new.", "default": "all" }, "proxyConfiguration": { "title": "Proxy Configuration", "type": "object", "description": "Optional proxy. Not needed for local runs. Enable if Reddit blocks your datacenter IP." } } }, "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 } } } } } } } } } } ```