# 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, NaN 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 ## 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 } } } } } } } } } } ```