# Threads 輿情爬蟲 — 個股情緒、熱門話題、繁中量化研究專用 (`claude_code_reviewer/threads-stock-sentiment`) Actor 用 Threads 做台股 / 加密 / 港股輿情監控？這隻 Actor 為量化研究員、券商分析師、AI 投顧、財經創作者設計：批量監控 100 個 ticker（台積電、輝達、聯發科、特斯拉、比特幣…），完整討論串 + 留言、ISO 時間戳、互動數據（讚、回覆、轉發、引用、觀看）直接進 pandas / BI / sentiment model。相對日期 7 天 / 1 個月排程友善。每筆 $0.005，比 RavenPack / Bloomberg sentiment 便宜兩個數量級。不用登入、不用 Threads API token。 - **URL**: https://apify.com/claude\_code\_reviewer/threads-stock-sentiment.md - **Developed by:** [Reggie](https://apify.com/claude_code_reviewer) (community) - **Categories:** AI, Social media, News - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 1 bookmarks - **User rating**: No ratings yet ## Pricing from $2.50 / 1,000 results This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events. 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 ## 台股 Threads 輿情爬蟲 > 個股情緒、熱門話題、繁中量化研究專用 — 用 Threads 公開資料補你的 sentiment pipeline --- ### 🎯 適合誰 | 角色 | 怎麼用 | |---|---| | **量化交易員 / 私募基金** | 個股 sentiment alpha 訊號、輿情突發事件偵測 | | **券商研究員** | 個股報告補 fundamental 之外的 social signal | | **AI 投顧 / fintech startup** | 訓練繁中 sentiment / topic-modeling 的 input | | **財經 KOL / YouTuber** | 找熱門股、預判 trending topic、選題 | | **PR / IR** | 監控自家上市公司被討論、危機預警 | | **新聞媒體財經線** | 新聞前哨、市場熱度指標 | --- ### 💡 三個典型 use case #### 1. 個股輿情每日掃 每天早盤前抓「台積電、鴻海、聯發科、輝達、台達電」過去 24 小時所有貼文 + 互動數據，丟進你的 sentiment dashboard。 ```json { "mode": "search", "keywords": ["台積電", "鴻海", "聯發科", "輝達", "台達電"], "dateFrom": "1 day", "searchSort": "top", "maxItemsPerSource": 200 } ```` 排程設成每天 08:30 跑一次、結果 webhook 到你的 BI 工具。 #### 2. 突發事件深 dig 某檔個股突然爆量，需要 dig 進前 10 篇熱門貼文的留言區看反方論述。 ```json { "mode": "post", "postUrls": [ "https://www.threads.com/@some_user/post/Cxxxxxx", "https://www.threads.com/@another_user/post/Cyyyyyy" ], "includeReplies": true } ``` #### 3. 季度法說熱度監測 抓 Q4 法說會前後 3 天「台積電 法說」、「聯發科 法說」等關鍵字，比對歷史 baseline。 ```json { "mode": "search", "keywords": ["台積電", "聯發科", "鴻海"], "dateFrom": "2026-01-13", "dateTo": "2026-01-19", "searchSort": "top" } ``` *** ### 📊 抓什麼資料 #### 每篇貼文 - `postId`、`postUrl`、`content`、`publishedAt`、**`publishedAtISO`** ← 直接 import pandas / SQL - `mediaType` (`text` / `photo` / `video` / `carousel`) + `mediaUrls[]` - 互動數據：`likeCount`、`replyCount`、`repostCount`、`shareCount`、`viewCount`、`quoteCount` - `sourceType`（哪個模式抓的）、`sourceQuery`（哪個 ticker / 帳號） - `scrapedAt` 時間戳 - `threadParts[]` — 多段 thread 自動 merge 成單筆 record #### 每個作者 - `username`、`displayName`、`bio`、`profilePictureUrl`、`followerCount` - `verified` 標記 #### 留言（`post` 模式啟用 `includeReplies` 時） - 每則留言完整 metadata + 互動數據 *** ### ⚙️ 五種模式 | 模式 | 用途 | 主要欄位 | |---|---|---| | **🔎 關鍵字搜尋**（預設）| 個股 / 主題輿情 — 量化常用 | `keywords[]` + 日期範圍 | | 🏷️ 標籤 / 話題 | `#AI概念股`、`#半導體` 等 hashtag | `keywords[]`（含 #） | | 👤 用戶主頁 | KOL / 分析師抓貼文 | `usernames[]` | | 💬 單篇貼文 + 留言 | 反方論述深 dig、突發事件分析 | `postUrls[]` + `includeReplies` | | 📰 自訂動態 feed 網址 | 已知 feed URL 直接抓 | `feedUrls[]` | *** ### 💰 為什麼是繁中市場最便宜的 sentiment data source | 服務 | 月費 / 單筆價 | 涵蓋繁中 Threads？ | |---|---|---| | Bloomberg Terminal | $2,000+/mo | ❌ | | RavenPack | $1,000+/mo | ⚠️ 有限 | | StockTwits API | $500/mo（美股 only）| ❌ | | **這個 Actor** | **$0.005 / 筆**（每月 1000 筆 = $5） | ✅ 原生 | *** ### 🔧 排程整合 支援相對日期 `7 days`、`1 month`、`2 weeks` — 在 n8n / Zapier / Make / Apify Schedules 排日 / 週 / 月 cron 都不用每次改參數。 *** ### 🧪 跟 generic Threads scraper 的差異 這個 actor 跟 [`threads-feed-scraper`](https://apify.com/claude_code_reviewer/threads-feed-scraper) **共用同一份 Docker image**，差異在 **預設配置 + 文件對 fintech 用法做了優化**： - 預設模式從 `user` 改成 `search`（多數 fintech use case 是 search） - 預設 prefill：`["台積電", "輝達", "聯發科", "#AI概念股"]`（一打開就 get use case） - README 寫法以個股輿情為主軸 如果你的 use case 不是 fintech、用一般版本即可。 *** ### 🤝 Support 有 bug / feature request：[GitHub issue](https://github.com/gn01763009/threads-feed-scraper/issues) # Actor input Schema ## `mode` (type: `string`): 要抓什麼?選一個模式,下面對應的欄位填一下就好。**多數 fintech use case 用「🔎 關鍵字搜尋」最直觀**(直接餵 ticker / 公司名),其他模式視需求。 ## `usernames` (type: `array`): Threads 純帳號(不用加 @ 也不用貼網址)。財經 KOL / 券商 / 分析師抓他們的貼文時用這個欄位。例如:財經 M 平方、產業隊長、股癌。 ## `bulkUsernames` (type: `string`): **一行一個帳號**,按 Enter 換行。**不要加引號、不要用逗號分隔。** 直接從你的監測名單 Excel 複製一整欄貼進來最快。執行時會自動併進上面的「帳號列表」。 ## `keywords` (type: `array`): 個股名稱、ticker、產業關鍵字、或 hashtag。例如:台積電、輝達、聯發科、#AI概念股、特斯拉、比特幣。⚠️ **一個概念一個關鍵字** — Threads 搜尋會把整串當一個詞匹配,複合詞像「台積電法說」會回 0 筆,要拆成「台積電」和「法說」分別跑。 ## `bulkKeywords` (type: `string`): **一行一個個股 / 關鍵字 / hashtag**,按 Enter 換行。**不要加引號、不要用逗號分隔。** 範例: ``` 台積電 鴻海 聯發科 輝達 特斯拉 #AI概念股 #半導體 ``` 直接從你的觀察清單 Google Sheet 複製貼上最快。執行時會自動併進上面的「個股 / 關鍵字」欄位。 ## `searchSort` (type: `string`): 關鍵字搜尋的排序方式,只有「搜尋」模式會吃這個設定。 ## `dateFrom` (type: `string`): 最早的貼文日期。可填 YYYY-MM-DD 絕對日期,或相對表示法:「7 days」、「1 month」、「2 weeks」。 ## `dateTo` (type: `string`): 最晚的貼文日期。格式同起始日期。 ## `postUrls` (type: `array`): Threads 貼文完整網址,會一併抓留言。例如:https://www.threads.com/@user/post/ABC123。 ## `feedUrls` (type: `array`): Threads 自訂 feed 網址。例如:https://www.threads.com/custom\_feed/18113589370710265。 ## `maxPosts` (type: `integer`): 每個來源最多抓幾篇貼文。越多費用越高 (每筆 $0.005)。捲動次數會自動算,不用手動調。 ## Actor input object example ```json { "mode": "search", "keywords": [ "台積電", "輝達", "聯發科", "#AI概念股" ], "searchSort": "top", "maxPosts": 50 } ``` # Actor output Schema ## `dataset` (type: `string`): No description # API You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup. ## JavaScript example ```javascript import { ApifyClient } from 'apify-client'; // Initialize the ApifyClient with your Apify API token // Replace the '' with your token const client = new ApifyClient({ token: '', }); // Prepare Actor input const input = { "mode": "search", "keywords": [ "台積電", "輝達", "聯發科", "#AI概念股" ] }; // Run the Actor and wait for it to finish const run = await client.actor("claude_code_reviewer/threads-stock-sentiment").call(input); // Fetch and print Actor results from the run's dataset (if any) console.log('Results from dataset'); console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${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 = { "mode": "search", "keywords": [ "台積電", "輝達", "聯發科", "#AI概念股", ], } # Run the Actor and wait for it to finish run = client.actor("claude_code_reviewer/threads-stock-sentiment").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 '{ "mode": "search", "keywords": [ "台積電", "輝達", "聯發科", "#AI概念股" ] }' | apify call claude_code_reviewer/threads-stock-sentiment --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=claude_code_reviewer/threads-stock-sentiment", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ````json { "openapi": "3.0.1", "info": { "title": "Threads 輿情爬蟲 — 個股情緒、熱門話題、繁中量化研究專用", "description": "用 Threads 做台股 / 加密 / 港股輿情監控？這隻 Actor 為量化研究員、券商分析師、AI 投顧、財經創作者設計：批量監控 100 個 ticker（台積電、輝達、聯發科、特斯拉、比特幣…），完整討論串 + 留言、ISO 時間戳、互動數據（讚、回覆、轉發、引用、觀看）直接進 pandas / BI / sentiment model。相對日期 7 天 / 1 個月排程友善。每筆 $0.005，比 RavenPack / Bloomberg sentiment 便宜兩個數量級。不用登入、不用 Threads API token。", "version": "1.0", "x-build-id": "KpYC7uLOWU1ajFqsZ" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/claude_code_reviewer~threads-stock-sentiment/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-claude_code_reviewer-threads-stock-sentiment", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for its completion, and returns Actor's dataset items in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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/claude_code_reviewer~threads-stock-sentiment/runs": { "post": { "operationId": "runs-sync-claude_code_reviewer-threads-stock-sentiment", "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/claude_code_reviewer~threads-stock-sentiment/run-sync": { "post": { "operationId": "run-sync-claude_code_reviewer-threads-stock-sentiment", "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": { "mode": { "title": "🎯 模式", "enum": [ "search", "hashtag", "user", "post", "feed" ], "type": "string", "description": "要抓什麼?選一個模式,下面對應的欄位填一下就好。**多數 fintech use case 用「🔎 關鍵字搜尋」最直觀**(直接餵 ticker / 公司名),其他模式視需求。", "default": "search" }, "usernames": { "title": "👤 帳號列表", "maxItems": 100, "uniqueItems": true, "type": "array", "description": "Threads 純帳號(不用加 @ 也不用貼網址)。財經 KOL / 券商 / 分析師抓他們的貼文時用這個欄位。例如:財經 M 平方、產業隊長、股癌。", "items": { "type": "string" } }, "bulkUsernames": { "title": "📋 批量貼帳號", "type": "string", "description": "**一行一個帳號**,按 Enter 換行。**不要加引號、不要用逗號分隔。** 直接從你的監測名單 Excel 複製一整欄貼進來最快。執行時會自動併進上面的「帳號列表」。" }, "keywords": { "title": "🏷️ 個股 / 關鍵字 / 標籤", "maxItems": 100, "uniqueItems": true, "type": "array", "description": "個股名稱、ticker、產業關鍵字、或 hashtag。例如:台積電、輝達、聯發科、#AI概念股、特斯拉、比特幣。⚠️ **一個概念一個關鍵字** — Threads 搜尋會把整串當一個詞匹配,複合詞像「台積電法說」會回 0 筆,要拆成「台積電」和「法說」分別跑。", "items": { "type": "string" } }, "bulkKeywords": { "title": "📋 批量貼個股 / 關鍵字", "type": "string", "description": "**一行一個個股 / 關鍵字 / hashtag**,按 Enter 換行。**不要加引號、不要用逗號分隔。** 範例:\n\n```\n台積電\n鴻海\n聯發科\n輝達\n特斯拉\n#AI概念股\n#半導體\n```\n\n直接從你的觀察清單 Google Sheet 複製貼上最快。執行時會自動併進上面的「個股 / 關鍵字」欄位。" }, "searchSort": { "title": "🔀 搜尋排序", "enum": [ "top", "recent" ], "type": "string", "description": "關鍵字搜尋的排序方式,只有「搜尋」模式會吃這個設定。", "default": "top" }, "dateFrom": { "title": "📅 起始日期", "type": "string", "description": "最早的貼文日期。可填 YYYY-MM-DD 絕對日期,或相對表示法:「7 days」、「1 month」、「2 weeks」。" }, "dateTo": { "title": "📅 結束日期", "type": "string", "description": "最晚的貼文日期。格式同起始日期。" }, "postUrls": { "title": "💬 貼文網址", "uniqueItems": true, "type": "array", "description": "Threads 貼文完整網址,會一併抓留言。例如:https://www.threads.com/@user/post/ABC123。", "items": { "type": "string" } }, "feedUrls": { "title": "📰 動態 feed 網址", "uniqueItems": true, "type": "array", "description": "Threads 自訂 feed 網址。例如:https://www.threads.com/custom_feed/18113589370710265。", "items": { "type": "string" } }, "maxPosts": { "title": "📊 最多抓幾篇", "minimum": 1, "maximum": 500, "type": "integer", "description": "每個來源最多抓幾篇貼文。越多費用越高 (每筆 $0.005)。捲動次數會自動算,不用手動調。", "default": 50 } } }, "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 } } } } } } } } } } ````