# Sentiment Analysis Classifier - Low-costπŸ’²πŸ”₯πŸ€–πŸ“ˆ (`delectable_incubator/sentiment-analysis-classifier-low-cost`) Actor πŸ€–πŸ“Š Analyze text sentiment with AI in seconds. Detect positive, negative, and neutral sentiment for each sentence while extracting sentiment scores and classifications. Ideal for customer feedback analysis, review monitoring, social media insights and API-powered sentiment intelligence. πŸš€ - **URL**: https://apify.com/delectable\_incubator/sentiment-analysis-classifier-low-cost.md - **Developed by:** [Prime Scrape](https://apify.com/delectable_incubator) (community) - **Categories:** AI, Automation, MCP servers - **Stats:** 1 total users, 0 monthly users, 0.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing from $0.00005 / actor start This Actor is paid per event and usage. You are charged both the fixed price for specific events and for Apify platform usage. Since this Actor supports Apify Store discounts, the price gets lower the higher subscription plan you have. 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

Sentiment Analysis Tool

--- ## πŸ€–πŸ“Š Sentiment Analysis Tool | Bulk Text Sentiment Analysis | Apify Actor ### πŸš€ Analyze Text Sentiment in Seconds (No Code) The **Sentiment Analysis Tool (Apify Actor)** is a powerful, scalable, and SEO-optimized AI text analysis tool that classifies large volumes of text into **Positive**, **Neutral**, or **Negative** sentiment. Analyze customer feedback, reviews, comments, emails, survey responses, social media posts, support tickets, AI datasets, and any other text in bulk. Export structured sentiment data for business intelligence, NLP projects, market research, customer experience monitoring, and AI applications. --- ## πŸ”₯ Why This Sentiment Analysis Tool? βœ” Best AI Sentiment Analysis tool on Apify βœ” Supports bulk text analysis (multi-text mode πŸš€) βœ” AI-powered sentiment classification βœ” Positive / Neutral / Negative detection βœ” Confidence scoring βœ” Automatic language detection βœ” Fast & scalable processing βœ” 100% structured JSON / CSV / Excel output βœ” Perfect for AI datasets & NLP projects βœ” No coding required --- ## 🎯 What This Tool Does This Apify Actor analyzes text using AI and automatically determines its sentiment. ### πŸ“Œ Core Features βœ… Bulk text sentiment analysis (SEO BOOST πŸš€) βœ… Analyze unlimited text inputs βœ… Positive / Neutral / Negative classification βœ… AI confidence score βœ… Automatic language detection βœ… Emotion strength analysis βœ… Word & sentence statistics βœ… Clean structured dataset output βœ… High-speed processing engine --- ## ⚑ Input Configuration (Simple & Powerful) ### πŸ”₯ BULK TEXT MODE (SEO BOOST πŸš€) ```` { "texts": \[ "I am so happy with this product!", "This is the worst experience I've ever had.", "The weather is nice today.", "I feel great about this decision.", "I am not sure if I made the right choice." ] } ``` --- ## πŸ“Š Extracted Analysis Data (Structured Output) | Field | Description | |------|-------------| | inputText | Original input text | | finalClassification | Positive / Neutral / Negative | | value | Overall sentiment score | | language | Detected language | | positiveScore | Positive confidence | | neutralScore | Neutral confidence | | negativeScore | Negative confidence | | emotionStrength | Emotion intensity | | wordCount | Number of words | | sentenceCount | Number of sentences | | languageDetectionConfidence | Language detection confidence | --- ## πŸ’‘ Use Cases (High Demand SEO Keywords) This AI sentiment analysis tool is perfect for: πŸ“± Social media monitoring ⭐ Customer review analysis πŸ›’ Product review analysis 🏨 Hotel review analysis 🍽 Restaurant review analysis πŸ’¬ Customer feedback analysis πŸ“§ Email sentiment detection 🎯 Brand reputation monitoring πŸ“Š Market research πŸ€– AI & NLP datasets 🧠 Machine Learning projects πŸ“ˆ Business intelligence ⚑ Bulk text sentiment analysis --- ## πŸš€ Key Features (Apify SEO Optimized) ⚑ Bulk text processing πŸ€– AI-powered sentiment analysis 🌍 Automatic language detection πŸ“Š Confidence scoring 🧠 Emotion strength analysis πŸ“Œ Structured output πŸ’Ύ Export-ready datasets πŸ” Reliable processing engine βš™οΈ Cloud scalable execution --- ## πŸ“€ Output Formats Supported βœ” JSON βœ” CSV βœ” Excel XLSX βœ” XML βœ” HTML --- ## πŸ“¦ Example Output ``` { "inputText": "I am so happy with this product!", "finalClassification": "Positive", "value": 0.6468, "language": "en", "positiveScore": 0.461, "neutralScore": 0.539, "negativeScore": 0, "emotionStrength": 0.461, "wordCount": 7, "sentenceCount": 1, "languageDetectionConfidence": 0.99 } ```` --- ## πŸ“Š Preconfigured Dataset Views ### 😊 Sentiment Overview Quick summary of analyzed texts. Fields included: β€’ Classification β€’ Sentiment Score β€’ Language β€’ Confidence --- ### πŸ“„ Detailed Analysis View Complete AI sentiment analysis. Fields included: β€’ Original Text β€’ Classification β€’ Positive Score β€’ Neutral Score β€’ Negative Score β€’ Emotion Strength β€’ Language β€’ Word Count β€’ Sentence Count --- ### 🌍 Language Detection View Analyze texts by detected language. Fields included: β€’ Language β€’ Detection Confidence β€’ Sentiment --- ### πŸ“ˆ Confidence Analysis View Evaluate AI prediction confidence. Fields included: β€’ Classification β€’ Confidence Scores β€’ Emotion Strength --- ## πŸ”₯ Why This is the BEST Sentiment Analysis Tool on Apify? βœ” Optimized for Apify Marketplace SEO βœ” AI-powered sentiment engine βœ” Bulk text analysis support βœ” Automatic language detection βœ” Enterprise-ready architecture βœ” Clean structured datasets βœ” Perfect for AI, NLP & Business Intelligence --- ## πŸ’Έ Pricing This scraper runs on a **pay-per-result pricing model**. You only pay for successfully extracted records. πŸ’³ **Price: $9.49 / 1,000 results** --- ## ❓ FAQ (SEO BOOST SECTION) #### Can I analyze multiple texts at once? Yes β€” Bulk Text Mode is fully supported. #### Does it classify sentiment automatically? Yes β€” every text is automatically classified as Positive, Neutral, or Negative. #### Does it detect the language? Yes β€” language detection is included automatically. #### Does it provide confidence scores? Yes β€” confidence values are returned for every prediction. #### Is the tool fast? Yes β€” optimized for high-speed AI text processing. #### Can I export the results? Yes β€” JSON, CSV, Excel, XML and HTML are supported. #### Is coding required? No β€” it's a 100% no-code Apify Actor. --- ## ⚠️ Disclaimer This tool is an independent AI text analysis solution and is not affiliated with any social media platform, review platform, or third-party service. --- ## πŸ”— Related Actors (PrimeScrape AI Intelligence Suite) We are building a complete **PrimeScrape AI Intelligence Suite**. πŸ‘‰ More AI-powered text analysis tools coming soon πŸš€ --- ## 🌍 PrimeScrape Ecosystem Built for AI, automation, data extraction, NLP, and business intelligence at scale. πŸ€– AI text analysis πŸ“Š Sentiment intelligence πŸ’¬ Customer feedback analysis πŸ“ˆ Market intelligence 🧠 AI datasets βš™οΈ Automation pipelines 🌍 Enterprise analytics --- ## πŸ“¬ Support ⭐⭐⭐⭐⭐ Leave a review if you enjoy this tool. πŸ“© Contact us for enterprise AI solutions, custom NLP workflows, or large-scale sentiment analysis projects. # Actor input Schema ## `Texts` (type: `array`): Analyze the sentiment of multiple texts and classify each as positive, negative, or neutral. ## Actor input object example ```json { "Texts": [ "I absolutely love this product. It exceeded all my expectations.", "The customer support was incredibly helpful and resolved my issue quickly.", "Everything arrived on time and in perfect condition.", "This app is intuitive, fast, and easy to use.", "I highly recommend this service to anyone.", "The quality is excellent for the price.", "I'm very satisfied with my purchase.", "Fantastic experience from start to finish.", "The food was delicious and the staff were friendly.", "This is one of the best investments I've made.", "The product stopped working after just two days.", "I'm extremely disappointed with the quality.", "Customer service never responded to my emails.", "This was a complete waste of money.", "The delivery was delayed by over a week.", "Nothing works as advertised.", "I regret buying this product.", "The website keeps crashing and is unusable.", "The item arrived damaged and poorly packaged.", "This has been the worst shopping experience I've ever had.", "The meeting starts at 10 AM tomorrow.", "Today's weather is cloudy with a chance of rain.", "I finished reading the report yesterday.", "The package is currently in transit.", "The train arrived at the station on schedule.", "This laptop weighs about 1.5 kilograms.", "Our office is located in downtown Chicago.", "The event will take place next Friday.", "There are five items left in stock.", "The document contains twenty pages.", "I'm not sure how I feel about the new update.", "The movie was okay, but it wasn't memorable.", "It could have been better, but it wasn't terrible either.", "I have mixed feelings about this decision.", "The results were different from what I expected.", "The experience was average overall.", "Some features are useful while others need improvement.", "I'm still deciding whether I like it.", "It's acceptable for occasional use.", "The performance is decent but inconsistent." ] } ```` # 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 = { "Texts": [ "I absolutely love this product. It exceeded all my expectations.", "The customer support was incredibly helpful and resolved my issue quickly.", "Everything arrived on time and in perfect condition.", "This app is intuitive, fast, and easy to use.", "I highly recommend this service to anyone.", "The quality is excellent for the price.", "I'm very satisfied with my purchase.", "Fantastic experience from start to finish.", "The food was delicious and the staff were friendly.", "This is one of the best investments I've made.", "The product stopped working after just two days.", "I'm extremely disappointed with the quality.", "Customer service never responded to my emails.", "This was a complete waste of money.", "The delivery was delayed by over a week.", "Nothing works as advertised.", "I regret buying this product.", "The website keeps crashing and is unusable.", "The item arrived damaged and poorly packaged.", "This has been the worst shopping experience I've ever had.", "The meeting starts at 10 AM tomorrow.", "Today's weather is cloudy with a chance of rain.", "I finished reading the report yesterday.", "The package is currently in transit.", "The train arrived at the station on schedule.", "This laptop weighs about 1.5 kilograms.", "Our office is located in downtown Chicago.", "The event will take place next Friday.", "There are five items left in stock.", "The document contains twenty pages.", "I'm not sure how I feel about the new update.", "The movie was okay, but it wasn't memorable.", "It could have been better, but it wasn't terrible either.", "I have mixed feelings about this decision.", "The results were different from what I expected.", "The experience was average overall.", "Some features are useful while others need improvement.", "I'm still deciding whether I like it.", "It's acceptable for occasional use.", "The performance is decent but inconsistent." ] }; // Run the Actor and wait for it to finish const run = await client.actor("delectable_incubator/sentiment-analysis-classifier-low-cost").call(input); // Fetch and print Actor results from the run's dataset (if any) console.log('Results from dataset'); console.log(`πŸ’Ύ 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); }); // πŸ“š Want to learn more πŸ“–? Go to β†’ 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 = { "Texts": [ "I absolutely love this product. It exceeded all my expectations.", "The customer support was incredibly helpful and resolved my issue quickly.", "Everything arrived on time and in perfect condition.", "This app is intuitive, fast, and easy to use.", "I highly recommend this service to anyone.", "The quality is excellent for the price.", "I'm very satisfied with my purchase.", "Fantastic experience from start to finish.", "The food was delicious and the staff were friendly.", "This is one of the best investments I've made.", "The product stopped working after just two days.", "I'm extremely disappointed with the quality.", "Customer service never responded to my emails.", "This was a complete waste of money.", "The delivery was delayed by over a week.", "Nothing works as advertised.", "I regret buying this product.", "The website keeps crashing and is unusable.", "The item arrived damaged and poorly packaged.", "This has been the worst shopping experience I've ever had.", "The meeting starts at 10 AM tomorrow.", "Today's weather is cloudy with a chance of rain.", "I finished reading the report yesterday.", "The package is currently in transit.", "The train arrived at the station on schedule.", "This laptop weighs about 1.5 kilograms.", "Our office is located in downtown Chicago.", "The event will take place next Friday.", "There are five items left in stock.", "The document contains twenty pages.", "I'm not sure how I feel about the new update.", "The movie was okay, but it wasn't memorable.", "It could have been better, but it wasn't terrible either.", "I have mixed feelings about this decision.", "The results were different from what I expected.", "The experience was average overall.", "Some features are useful while others need improvement.", "I'm still deciding whether I like it.", "It's acceptable for occasional use.", "The performance is decent but inconsistent.", ] } # Run the Actor and wait for it to finish run = client.actor("delectable_incubator/sentiment-analysis-classifier-low-cost").call(run_input=run_input) # Fetch and print Actor results from the run's dataset (if there are any) print("πŸ’Ύ Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"]) for item in client.dataset(run["defaultDatasetId"]).iterate_items(): print(item) # πŸ“š Want to learn more πŸ“–? Go to β†’ https://docs.apify.com/api/client/python/docs/quick-start ``` ## CLI example ```bash echo '{ "Texts": [ "I absolutely love this product. It exceeded all my expectations.", "The customer support was incredibly helpful and resolved my issue quickly.", "Everything arrived on time and in perfect condition.", "This app is intuitive, fast, and easy to use.", "I highly recommend this service to anyone.", "The quality is excellent for the price.", "I'\''m very satisfied with my purchase.", "Fantastic experience from start to finish.", "The food was delicious and the staff were friendly.", "This is one of the best investments I'\''ve made.", "The product stopped working after just two days.", "I'\''m extremely disappointed with the quality.", "Customer service never responded to my emails.", "This was a complete waste of money.", "The delivery was delayed by over a week.", "Nothing works as advertised.", "I regret buying this product.", "The website keeps crashing and is unusable.", "The item arrived damaged and poorly packaged.", "This has been the worst shopping experience I'\''ve ever had.", "The meeting starts at 10 AM tomorrow.", "Today'\''s weather is cloudy with a chance of rain.", "I finished reading the report yesterday.", "The package is currently in transit.", "The train arrived at the station on schedule.", "This laptop weighs about 1.5 kilograms.", "Our office is located in downtown Chicago.", "The event will take place next Friday.", "There are five items left in stock.", "The document contains twenty pages.", "I'\''m not sure how I feel about the new update.", "The movie was okay, but it wasn'\''t memorable.", "It could have been better, but it wasn'\''t terrible either.", "I have mixed feelings about this decision.", "The results were different from what I expected.", "The experience was average overall.", "Some features are useful while others need improvement.", "I'\''m still deciding whether I like it.", "It'\''s acceptable for occasional use.", "The performance is decent but inconsistent." ] }' | apify call delectable_incubator/sentiment-analysis-classifier-low-cost --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=delectable_incubator/sentiment-analysis-classifier-low-cost", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Sentiment Analysis Classifier - Low-costπŸ’²πŸ”₯πŸ€–πŸ“ˆ", "description": "πŸ€–πŸ“Š Analyze text sentiment with AI in seconds. Detect positive, negative, and neutral sentiment for each sentence while extracting sentiment scores and classifications. Ideal for customer feedback analysis, review monitoring, social media insights and API-powered sentiment intelligence. πŸš€", "version": "0.0", "x-build-id": "QvWxyJMV60aKjqCIu" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/delectable_incubator~sentiment-analysis-classifier-low-cost/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-delectable_incubator-sentiment-analysis-classifier-low-cost", "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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/delectable_incubator~sentiment-analysis-classifier-low-cost/runs": { "post": { "operationId": "runs-sync-delectable_incubator-sentiment-analysis-classifier-low-cost", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/delectable_incubator~sentiment-analysis-classifier-low-cost/run-sync": { "post": { "operationId": "run-sync-delectable_incubator-sentiment-analysis-classifier-low-cost", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": [ "Texts" ], "properties": { "Texts": { "title": "Texts to analyze", "type": "array", "description": "Analyze the sentiment of multiple texts and classify each as positive, negative, or neutral.", "default": [ "I absolutely love this product. It exceeded all my expectations.", "The customer support was incredibly helpful and resolved my issue quickly.", "Everything arrived on time and in perfect condition.", "This app is intuitive, fast, and easy to use.", "I highly recommend this service to anyone.", "The quality is excellent for the price.", "I'm very satisfied with my purchase.", "Fantastic experience from start to finish.", "The food was delicious and the staff were friendly.", "This is one of the best investments I've made.", "The product stopped working after just two days.", "I'm extremely disappointed with the quality.", "Customer service never responded to my emails.", "This was a complete waste of money.", "The delivery was delayed by over a week.", "Nothing works as advertised.", "I regret buying this product.", "The website keeps crashing and is unusable.", "The item arrived damaged and poorly packaged.", "This has been the worst shopping experience I've ever had.", "The meeting starts at 10 AM tomorrow.", "Today's weather is cloudy with a chance of rain.", "I finished reading the report yesterday.", "The package is currently in transit.", "The train arrived at the station on schedule.", "This laptop weighs about 1.5 kilograms.", "Our office is located in downtown Chicago.", "The event will take place next Friday.", "There are five items left in stock.", "The document contains twenty pages.", "I'm not sure how I feel about the new update.", "The movie was okay, but it wasn't memorable.", "It could have been better, but it wasn't terrible either.", "I have mixed feelings about this decision.", "The results were different from what I expected.", "The experience was average overall.", "Some features are useful while others need improvement.", "I'm still deciding whether I like it.", "It's acceptable for occasional use.", "The performance is decent but inconsistent." ], "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 } } } } } } } } } } ```