# 📈 Twitter Sentiment & Narrative Report (`sentimentalpha/x-sentiment-narrative-report`) Actor Get one clean, scored JSON report with real-time X (Twitter) sentiment, dominant narratives, volume trends, key voices, and crisis flags for any ticker, brand, or topic — ready-to-use intelligence instead of parsing raw tweets from scrapers. - **URL**: https://apify.com/sentimentalpha/x-sentiment-narrative-report.md - **Developed by:** [Sentiment Alpha](https://apify.com/sentimentalpha) (community) - **Categories:** AI, Automation, Social media - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing from $250.00 / 1,000 sentiment & narrative reports This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events. 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 ## Twitter Sentiment Analysis & Narrative Reports — AI-Scored, No Scraping **Raw scrapers sell you 5,000 tweets. This sells you the answer.** This actor by SentimentAlpha runs real-time **Twitter sentiment analysis** on any ticker, brand, or topic and returns one decision-ready report: scored sentiment, the narratives driving the conversation, who's amplifying them, and a crisis flag if a negative story is picking up speed. Live from X (Twitter) via Grok's official X search — no scraping, no proxies, nothing that breaks when X changes its site. ### The math | | Raw tweet scraper | This actor | |---|---|---| | You pay | ~$2.00 (5,000 tweets @ $0.40/1K) | **$0.25 (one report)** | | You receive | 5,000 rows to clean, dedupe & analyze | The finished analysis | | Your time | ~1 hour (or another AI pipeline) | 0 minutes | | Breaks when X changes HTML? | Yes | No — official Grok API | | Answer to "is sentiment turning?" | Eventually | In the first field | ### What one $0.25 report contains ```json { "query": "$TSLA", "sentimentScore": -0.34, "sentimentLabel": "negative", "confidence": 0.81, "narrativeVelocity": 72, "crisisFlag": true, "crisisNote": "Delivery-miss narrative accelerating, amplified by three high-reach accounts in the last 6 hours.", "topNarratives": [ { "theme": "Q2 delivery miss", "stance": "negative", "strength": 84, "samplePost": "Third quarter in a row deliveries came in under whisper numbers..." } ], "keyVoices": [ { "handle": "@...", "stance": "bearish", "reach": "high" } ], "summary": "Sentiment has turned negative over the last 24h, driven primarily by..." } ```` ### Who uses this - **Brand, PR & comms teams** — "Is X turning against us? Is this a crisis or noise?" One run answers it. Schedule it daily and pipe alerts to Slack. - **Marketing agencies** — client-ready social snapshots without an $800/mo listening-tool seat. Run 40 client reports for $10. - **Traders & analysts** — which narratives are moving $NVDA, bitcoin, or a memecoin right now, at what velocity, and who's driving them. - **AI agents & automations** — clean structured JSON, callable by API, MCP, or schedule. Built for machine consumption. ### Why it's priced per report, not per tweet You're not buying data — you're buying the hour you'd spend turning data into a conclusion. Every report is a fresh, live Grok analysis of the current X conversation. You're charged **only for successful reports**; failed queries cost nothing. No subscription, no minimum: one report is $0.25, forty are $10. ### Get started in 60 seconds 1. Enter one or more queries: `Nike`, `$TSLA`, `GLP-1 drugs`, `@YourBrand` 2. Pick a timeframe (4h / 24h / 7d) 3. Run. Export JSON/CSV, connect to Sheets, Slack, or your agent — or schedule it and never check manually again. ### Twitter sentiment analysis FAQ #### What is Twitter sentiment analysis? Twitter sentiment analysis uses AI to classify posts about a brand, ticker, or topic as positive, negative, or neutral — and, in this actor's case, to extract the underlying narratives driving the conversation. It turns raw social noise into quantifiable signals for trading, PR, and competitive monitoring. #### How accurate is AI Twitter sentiment analysis? Modern models achieve high reliability on clear sentiment and emerging narratives when given fresh, high-engagement posts, which is exactly what this actor feeds them via Grok's live X search. Every report includes a calibrated confidence score so you know how much weight to put on each reading. #### Can I use Twitter sentiment for stock or crypto trading signals? Sentiment and narrative velocity are leading indicators in narrative-driven assets, but no single signal is a complete strategy. Best results come from layering sentiment velocity and contrarian divergence with price and volume data. #### What's the difference between X and Twitter sentiment analysis tools? Same platform — "X" is the current name, but most people still search "Twitter." This actor analyzes the live X platform regardless of what you call it. #### Is there a Twitter sentiment analysis API? Yes — this actor is one: call it via Apify's HTTP API, schedule it, or connect it through MCP for AI agents. You get scored, structured JSON without managing your own scraping or LLM pipeline. #### Do I need a Twitter/X API key to run this? No. The actor uses Grok's official X search under the hood, so there's nothing to configure — enter a query and run. *** **About SentimentAlpha:** we build AI analysis actors on Grok's official X search — finished intelligence, not raw data dumps. More actors coming: scheduled Brand Monitor and KOL Stance Tracker. [sentimentalpha.ai](https://sentimentalpha.ai) *Searches that find this actor: twitter sentiment analysis, twitter sentiment analysis api, x sentiment api, stock sentiment report, brand monitoring twitter, social listening actor, crypto twitter sentiment, narrative analysis, crisis detection, KOL tracking, tweet analysis without scraping.* # Actor input Schema ## `queries` (type: `array`): One report is generated (and charged) per query. Examples: 'Nike', '$TSLA', 'GLP-1 drugs', '@YourCompany'. ## `timeframe` (type: `string`): How far back to scan X activity. ## `includeNarratives` (type: `boolean`): Adds up to 5 ranked narratives (theme, stance, strength, sample post) per query. ## `includeKeyVoices` (type: `boolean`): Adds the most influential accounts driving the conversation and their stance. ## Actor input object example ```json { "queries": [ "$TSLA" ], "timeframe": "24h", "includeNarratives": true, "includeKeyVoices": true } ``` # 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 = { "queries": [ "$TSLA" ] }; // Run the Actor and wait for it to finish const run = await client.actor("sentimentalpha/x-sentiment-narrative-report").call(input); // Fetch and print Actor results from the run's dataset (if any) console.log('Results from dataset'); console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${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 = { "queries": ["$TSLA"] } # Run the Actor and wait for it to finish run = client.actor("sentimentalpha/x-sentiment-narrative-report").call(run_input=run_input) # Fetch and print Actor results from the run's dataset (if there are any) print("💾 Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"]) for item in client.dataset(run["defaultDatasetId"]).iterate_items(): print(item) # 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start ``` ## CLI example ```bash echo '{ "queries": [ "$TSLA" ] }' | apify call sentimentalpha/x-sentiment-narrative-report --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=sentimentalpha/x-sentiment-narrative-report", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "📈 Twitter Sentiment & Narrative Report", "description": "Get one clean, scored JSON report with real-time X (Twitter) sentiment, dominant narratives, volume trends, key voices, and crisis flags for any ticker, brand, or topic — ready-to-use intelligence instead of parsing raw tweets from scrapers.", "version": "0.1", "x-build-id": "ImWVbqs1MBEnyPm25" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/sentimentalpha~x-sentiment-narrative-report/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-sentimentalpha-x-sentiment-narrative-report", "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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/sentimentalpha~x-sentiment-narrative-report/runs": { "post": { "operationId": "runs-sync-sentimentalpha-x-sentiment-narrative-report", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/sentimentalpha~x-sentiment-narrative-report/run-sync": { "post": { "operationId": "run-sync-sentimentalpha-x-sentiment-narrative-report", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": [ "queries" ], "properties": { "queries": { "title": "Brands, tickers, or topics", "type": "array", "description": "One report is generated (and charged) per query. Examples: 'Nike', '$TSLA', 'GLP-1 drugs', '@YourCompany'.", "items": { "type": "string" } }, "timeframe": { "title": "Timeframe", "enum": [ "4h", "24h", "7d" ], "type": "string", "description": "How far back to scan X activity.", "default": "24h" }, "includeNarratives": { "title": "Include top narratives breakdown", "type": "boolean", "description": "Adds up to 5 ranked narratives (theme, stance, strength, sample post) per query.", "default": true }, "includeKeyVoices": { "title": "Include key voices (KOLs)", "type": "boolean", "description": "Adds the most influential accounts driving the conversation and their stance.", "default": true } } }, "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 } } } } } } } } } } ```