# TikTok & Instagram Trend Analyzer Premium (`tempting_finch/tiktok-instagram-trend-analyzer`) Actor Premium TikTok & Instagram trend intelligence. Discover trending hashtags, sounds, creators, and content patterns with advanced engagement analytics, PDF/HTML reports, and gap analysis. - **URL**: https://apify.com/tempting\_finch/tiktok-instagram-trend-analyzer.md - **Developed by:** [Karl Goyer](https://apify.com/tempting_finch) (community) - **Categories:** Automation, Developer tools, AI - **Stats:** 2 total users, 1 monthly users, 100.0% 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. 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/docs.md): ```bash npm install apify-client ``` In Python projects, use official [Python client library](https://docs.apify.com/api/client/python/docs.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/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 ## TikTok & Instagram Trend Analyzer Premium [![Deploy to Apify](https://img.shields.io/badge/Apify-Deploy-blue)](https://console.apify.com/actors/hTzjVPJUwbi604xY2) **Premium cross-platform trend intelligence for TikTok & Instagram.** Discover trending hashtags, sounds, creators and content patterns with advanced engagement analytics, growth metrics, competitive benchmarking, PDF/HTML reports, and content gap analysis. ### Features - **Multi-Platform Analysis** — TikTok and Instagram in a single run - **5 Analysis Modes**: Trending Hashtags, Trending Topics, Content Pattern Analysis, Competitor Analysis, Full Audit - **Advanced Metrics**: Engagement rates, growth trends, virality scores, sentiment analysis - **Smart Filtering**: Filter by engagement rate, followers, lookback period - **Cross-Platform Insights**: Identify trends spanning both platforms - **Export**: Downloadable JSON and CSV reports ### Input | Parameter | Type | Default | Description | |---|---|---|---| | `platforms` | `string[]` | `["tiktok","instagram"]` | Platforms to analyze | | `searchType` | `string` | `trending_hashtags` | Analysis mode | | `query` | `string` | `""` | Keyword, hashtag, or niche (empty = global) | | `maxResults` | `integer` | `50` | Max items per platform | | `lookbackDays` | `integer` | `7` | Analysis time window | | `minEngagementRate` | `number` | `0` | Minimum engagement rate filter | | `minFollowers` | `integer` | `0` | Minimum follower count filter | | `includeMetrics` | `boolean` | `true` | Include advanced analytics | | `exportFormats` | `string[]` | `["json","csv"]` | Export file formats | ### Output Each run produces: - **Dataset** — Rich JSON records per platform with full trend data - **Key-Value Store** — Downloadable JSON and CSV exports - **Summary** — Top hashtags, creators, engagement benchmarks - **Recommendations** — Actionable insights for content strategy ### Analysis Modes | Mode | Description | |---|---| | `trending_hashtags` | Discover fastest-growing hashtags with engagement data | | `trending_topics` | Identify trending themes and conversations | | `content_analysis` | Analyze top-performing content and creator strategies | | `competitor_analysis` | Benchmark competitor performance | | `full_audit` | Everything — comprehensive platform audit | ### Run Locally ```bash pip install -r requirements.txt python -m src.main ```` With Apify CLI: ```bash apify run ``` ### Deploy ```bash apify login apify push ``` ### License MIT # Actor input Schema ## `platforms` (type: `array`): Select which platforms to analyze. Cross-platform analysis yields richer insights. ## `searchType` (type: `string`): Type of trend analysis to perform. ## `query` (type: `string`): Keyword, hashtag, or topic to analyze. Leave empty for global trending. ## `country` (type: `string`): Geo-target the trend analysis to a specific region for localized insights. ## `contentType` (type: `string`): Filter by content format type. ## `sortBy` (type: `string`): How to rank the results. ## `maxResults` (type: `integer`): Maximum number of items per platform to analyze. ## `lookbackDays` (type: `integer`): How many days back to analyze trends. ## `minEngagementRate` (type: `number`): Minimum engagement rate filter (0 = no filter). ## `minFollowers` (type: `integer`): Minimum follower count filter (0 = no filter). ## `includeMetrics` (type: `boolean`): Include advanced analytics: growth rates, sentiment, virality scores, content gap analysis, and trend lifecycle predictions. ## `includePdfReport` (type: `boolean`): Generate a professional PDF report with charts, tables, and branded styling. ## `includeHtmlDashboard` (type: `boolean`): Generate an interactive HTML dashboard with Chart.js visualizations. ## `exportFormats` (type: `array`): Generate downloadable export files in these formats. ## `proxy` (type: `string`): Optional proxy URL for requests (e.g., http://user:pass@host:port). ## Actor input object example ```json { "platforms": [ "tiktok", "instagram" ], "searchType": "trending_hashtags", "query": "fashion", "country": "global", "contentType": "all", "sortBy": "trending", "maxResults": 50, "lookbackDays": 7, "minEngagementRate": 0, "minFollowers": 0, "includeMetrics": true, "includePdfReport": true, "includeHtmlDashboard": true, "exportFormats": [ "json", "csv" ], "proxy": "" } ``` # 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 = { "platforms": [ "tiktok", "instagram" ], "query": "fashion", "exportFormats": [ "json", "csv" ] }; // Run the Actor and wait for it to finish const run = await client.actor("tempting_finch/tiktok-instagram-trend-analyzer").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 = { "platforms": [ "tiktok", "instagram", ], "query": "fashion", "exportFormats": [ "json", "csv", ], } # Run the Actor and wait for it to finish run = client.actor("tempting_finch/tiktok-instagram-trend-analyzer").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 '{ "platforms": [ "tiktok", "instagram" ], "query": "fashion", "exportFormats": [ "json", "csv" ] }' | apify call tempting_finch/tiktok-instagram-trend-analyzer --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=tempting_finch/tiktok-instagram-trend-analyzer", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "TikTok & Instagram Trend Analyzer Premium", "description": "Premium TikTok & Instagram trend intelligence. Discover trending hashtags, sounds, creators, and content patterns with advanced engagement analytics, PDF/HTML reports, and gap analysis.", "version": "2.0", "x-build-id": "TAJ58cMsnWwDoOIQp" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/tempting_finch~tiktok-instagram-trend-analyzer/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-tempting_finch-tiktok-instagram-trend-analyzer", "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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/tempting_finch~tiktok-instagram-trend-analyzer/runs": { "post": { "operationId": "runs-sync-tempting_finch-tiktok-instagram-trend-analyzer", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/tempting_finch~tiktok-instagram-trend-analyzer/run-sync": { "post": { "operationId": "run-sync-tempting_finch-tiktok-instagram-trend-analyzer", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": [ "platforms", "searchType" ], "properties": { "platforms": { "title": "Platforms", "type": "array", "description": "Select which platforms to analyze. Cross-platform analysis yields richer insights.", "items": { "type": "string" }, "default": [ "tiktok", "instagram" ] }, "searchType": { "title": "Search Type", "enum": [ "trending_hashtags", "trending_topics", "content_analysis", "competitor_analysis", "full_audit" ], "type": "string", "description": "Type of trend analysis to perform.", "default": "trending_hashtags" }, "query": { "title": "Search Query / Niche", "type": "string", "description": "Keyword, hashtag, or topic to analyze. Leave empty for global trending.", "default": "" }, "country": { "title": "Country / Region", "enum": [ "global", "us", "uk", "fr", "de", "jp", "br", "in", "ca", "au" ], "type": "string", "description": "Geo-target the trend analysis to a specific region for localized insights.", "default": "global" }, "contentType": { "title": "Content Type Filter", "enum": [ "all", "video", "image", "carousel", "reel" ], "type": "string", "description": "Filter by content format type.", "default": "all" }, "sortBy": { "title": "Sort Results By", "enum": [ "trending", "engagement", "growth", "virality" ], "type": "string", "description": "How to rank the results.", "default": "trending" }, "maxResults": { "title": "Max Results", "minimum": 5, "maximum": 500, "type": "integer", "description": "Maximum number of items per platform to analyze.", "default": 50 }, "lookbackDays": { "title": "Lookback Period (Days)", "minimum": 1, "maximum": 90, "type": "integer", "description": "How many days back to analyze trends.", "default": 7 }, "minEngagementRate": { "title": "Min Engagement Rate (%)", "minimum": 0, "maximum": 100, "type": "number", "description": "Minimum engagement rate filter (0 = no filter).", "default": 0 }, "minFollowers": { "title": "Min Followers", "minimum": 0, "type": "integer", "description": "Minimum follower count filter (0 = no filter).", "default": 0 }, "includeMetrics": { "title": "Advanced Analytics", "type": "boolean", "description": "Include advanced analytics: growth rates, sentiment, virality scores, content gap analysis, and trend lifecycle predictions.", "default": true }, "includePdfReport": { "title": "PDF Report", "type": "boolean", "description": "Generate a professional PDF report with charts, tables, and branded styling.", "default": true }, "includeHtmlDashboard": { "title": "HTML Dashboard", "type": "boolean", "description": "Generate an interactive HTML dashboard with Chart.js visualizations.", "default": true }, "exportFormats": { "title": "Data Export Formats", "type": "array", "description": "Generate downloadable export files in these formats.", "items": { "type": "string" }, "default": [ "json", "csv" ] }, "proxy": { "title": "Proxy Configuration", "type": "string", "description": "Optional proxy URL for requests (e.g., http://user:pass@host:port).", "default": "" } } }, "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 } } } } } } } } } } ```