HumationHuman + Automation
Back to Catalog

Automate Twitter sentiment analysis with OpenAI, Google Sheets, and Slack alerts

InfyOm TechnologiesInfyOm Technologies
350 views
2/3/2026
Google DriveIMAPAutomationAttachmentsEmail
Official Page

✅ What problem does this workflow solve?

Tracking what people say about your brand on Twitter can be overwhelming, especially when important mentions slip through the cracks. This workflow automates the process: it scrapes Twitter mentions, analyzes sentiment using OpenAI, logs everything in a Google Sheet, and sends real-time Slack alerts for negative tweets. No manual monitoring needed.

⚙️ What does this workflow do?

  1. Runs on a schedule to monitor Twitter mentions or hashtags.
  2. Uses Apify to scrape tweets based on brand keywords.
  3. Filters out tweets already processed (avoids duplicates).
  4. Performs sentiment analysis with OpenAI (Positive, Neutral, Negative).
  5. Logs tweet content, sentiment, and reply (if any) in a Google Sheet.
  6. Sends an instant Slack notification for negative tweets.
  7. Generates thank-you replies for positive tweets and logs them.

🔧 Setup Instructions

🗓 Schedule Trigger

  • Use the Cron node to schedule checks (e.g., every hour, daily).

🐦 Apify Twitter Scraper Setup

  • Sign up on Apify
  • Generate your Apify API Token and use it in the HTTP node to run the actor and get tweet results.

🤖 OpenAI Sentiment Analysis

  • Get your API key from OpenAI

📄 Google Sheet Configuration

Prepare a Google Sheet with this sample format.

Connect it using the Google Sheets node in n8n.

💬 Slack Notifications

  • Connect your Slack workspace via the Slack node.
  • Set up the channel where negative tweets should be sent as alerts.

🧠 How it Works

1. Scheduled Run

Triggered at a fixed interval using the Schedule (Cron) node.

2. Scrape Mentions from Twitter

  • The Apify actor runs and collects recent tweets mentioning your brand or using your hashtag.
  • Links to the tweets are extracted.

3. Filter Previously Seen Tweets

  • Each tweet is checked against the Google Sheet.
  • If already present, it’s skipped to avoid duplicate analysis.

4. Analyze Sentiment with OpenAI

  • For new tweets, sentiment is classified into:
    • ✅ Positive
    • ⚪ Neutral
    • ❌ Negative

5. Store Results in Google Sheet

  • The tweet link, content, and sentiment are stored in a row.
  • If sentiment is positive, a thank-you reply is also generated and saved.

6. Notify Slack for Negative Tweets

  • When a tweet is tagged Negative, a Slack message is sent to the designated channel with the tweet link.

👤 Who can use this?

This workflow is ideal for:

  • 📢 Social Media Teams
  • 🧠 PR and Brand Managers
  • 🧑‍💻 Solo Founders
  • 🏢 Startups & SaaS Companies

Stay ahead of your brand's reputation—automatically.

🛠 Customization Ideas

  • 🎯 Add filters for specific campaign hashtags.
  • 📬 Send weekly summary reports via email.
  • 📥 Auto-open support tickets for negative mentions.
  • 🗣 Expand sentiment categories with more detailed tagging.

🚀 Ready to get started?

Just plug in:

  • 🔑 Your Apify API Token
  • 🔑 Your OpenAI API Key
  • 📄 Your Google Sheet
  • 💬 Your Slack Channel

Then deploy the workflow, and let it monitor Twitter for you!

Automated Twitter Sentiment Analysis with OpenAI, Google Sheets, and Slack Alerts

This n8n workflow automates the process of analyzing sentiment from Twitter data, storing the results in Google Sheets, and sending alerts to Slack for specific sentiment categories. It leverages OpenAI's language models for sentiment analysis, making it a powerful tool for monitoring brand perception, campaign performance, or general public opinion.

What it does

This workflow streamlines the sentiment analysis process through the following steps:

  1. Triggers on a Schedule: The workflow runs periodically (e.g., every hour, daily) to fetch new data.
  2. Fetches Data from Google Sheets: It reads a specified range of data from a Google Sheet, likely containing Twitter tweets or other text for analysis.
  3. Processes Data in Batches: To handle large datasets efficiently and manage API limits, the workflow splits the incoming data into manageable batches.
  4. Analyzes Sentiment with OpenAI: For each item in a batch, it uses an OpenAI Chat Model to perform sentiment analysis. The model is configured to output a structured JSON object containing the sentiment (e.g., "Positive", "Negative", "Neutral") and a brief explanation.
  5. Parses OpenAI Output: A Structured Output Parser extracts the sentiment and explanation from OpenAI's response.
  6. Categorizes Sentiment: A Switch node categorizes the sentiment into "Positive", "Negative", or "Neutral" based on the OpenAI output.
  7. Updates Google Sheets:
    • For "Positive" and "Neutral" sentiments, it appends the original text, sentiment, and explanation to a "Processed" sheet in Google Sheets.
    • For "Negative" sentiments, it appends the data to a "Negative" sheet in Google Sheets.
  8. Sends Slack Alerts for Negative Sentiment: If the sentiment is "Negative", the workflow sends a detailed alert to a specified Slack channel, including the original text, the detected negative sentiment, and OpenAI's explanation.

Prerequisites/Requirements

To use this workflow, you will need:

  • n8n Instance: A running instance of n8n.
  • OpenAI API Key: An API key for OpenAI to access their language models for sentiment analysis.
  • Google Account: A Google account with access to Google Sheets. You will need to create or specify the spreadsheet and sheets for input and output.
  • Slack Account: A Slack account and a channel where you want to receive alerts.
  • Google Sheets Credentials: Configured Google Sheets credentials in n8n.
  • OpenAI Credentials: Configured OpenAI credentials in n8n.
  • Slack Credentials: Configured Slack credentials in n8n.

Setup/Usage

  1. Import the Workflow: Download the workflow JSON and import it into your n8n instance.
  2. Configure Credentials:
    • Google Sheets: Set up your Google Sheets credentials. You'll need to authorize n8n to access your Google Sheets.
    • OpenAI: Set up your OpenAI API key as a credential in n8n.
    • Slack: Set up your Slack credentials. You'll need to authorize n8n to post messages to your Slack workspace.
  3. Update Google Sheets Node (ID: 18):
    • Specify the Spreadsheet ID and Sheet Name where your raw Twitter data or text for analysis is located.
    • Adjust the Range if necessary to read the relevant data.
  4. Configure AI Agent (ID: 1119):
    • Ensure your OpenAI Chat Model (ID: 1153) is correctly configured with your OpenAI credential.
    • Review the prompt for the AI Agent to ensure it aligns with your desired sentiment analysis criteria.
  5. Configure Google Sheets Nodes (for output):
    • For the "Processed" and "Negative" sentiment branches, update the Spreadsheet ID and Sheet Names to where you want the results to be written.
  6. Configure Slack Node (ID: 40):
    • Specify the Channel where negative sentiment alerts should be posted.
  7. Activate the Workflow: Once all configurations are complete, activate the workflow. It will run according to the schedule defined in the "Schedule Trigger" node (ID: 839). You can also manually test it by clicking "Execute Workflow".

Related Templates

Track competitor SEO keywords with Decodo + GPT-4.1-mini + Google Sheets

This workflow automates competitor keyword research using OpenAI LLM and Decodo for intelligent web scraping. Who this is for SEO specialists, content strategists, and growth marketers who want to automate keyword research and competitive intelligence. Marketing analysts managing multiple clients or websites who need consistent SEO tracking without manual data pulls. Agencies or automation engineers using Google Sheets as an SEO data dashboard for keyword monitoring and reporting. What problem this workflow solves Tracking competitor keywords manually is slow and inconsistent. Most SEO tools provide limited API access or lack contextual keyword analysis. This workflow solves that by: Automatically scraping any competitor’s webpage with Decodo. Using OpenAI GPT-4.1-mini to interpret keyword intent, density, and semantic focus. Storing structured keyword insights directly in Google Sheets for ongoing tracking and trend analysis. What this workflow does Trigger — Manually start the workflow or schedule it to run periodically. Input Setup — Define the website URL and target country (e.g., https://dev.to, france). Data Scraping (Decodo) — Fetch competitor web content and metadata. Keyword Analysis (OpenAI GPT-4.1-mini) Extract primary and secondary keywords. Identify focus topics and semantic entities. Generate a keyword density summary and SEO strength score. Recommend optimization and internal linking opportunities. Data Structuring — Clean and convert GPT output into JSON format. Data Storage (Google Sheets) — Append structured keyword data to a Google Sheet for long-term tracking. Setup Prerequisites If you are new to Decode, please signup on this link visit.decodo.com n8n account with workflow editor access Decodo API credentials OpenAI API key Google Sheets account connected via OAuth2 Make sure to install the Decodo Community node. Create a Google Sheet Add columns for: primarykeywords, seostrengthscore, keyworddensity_summary, etc. Share with your n8n Google account. Connect Credentials Add credentials for: Decodo API credentials - You need to register, login and obtain the Basic Authentication Token via Decodo Dashboard OpenAI API (for GPT-4o-mini) Google Sheets OAuth2 Configure Input Fields Edit the “Set Input Fields” node to set your target site and region. Run the Workflow Click Execute Workflow in n8n. View structured results in your connected Google Sheet. How to customize this workflow Track Multiple Competitors → Use a Google Sheet or CSV list of URLs; loop through them using the Split In Batches node. Add Language Detection → Add a Gemini or GPT node before keyword analysis to detect content language and adjust prompts. Enhance the SEO Report → Expand the GPT prompt to include backlink insights, metadata optimization, or readability checks. Integrate Visualization → Connect your Google Sheet to Looker Studio for SEO performance dashboards. Schedule Auto-Runs → Use the Cron Node to run weekly or monthly for competitor keyword refreshes. Summary This workflow automates competitor keyword research using: Decodo for intelligent web scraping OpenAI GPT-4.1-mini for keyword and SEO analysis Google Sheets for live tracking and reporting It’s a complete AI-powered SEO intelligence pipeline ideal for teams that want actionable insights on keyword gaps, optimization opportunities, and content focus trends, without relying on expensive SEO SaaS tools.

Ranjan DailataBy Ranjan Dailata
161

Generate song lyrics and music from text prompts using OpenAI and Fal.ai Minimax

Spark your creativity instantly in any chat—turn a simple prompt like "heartbreak ballad" into original, full-length lyrics and a professional AI-generated music track, all without leaving your conversation. 📋 What This Template Does This chat-triggered workflow harnesses AI to generate detailed, genre-matched song lyrics (at least 600 characters) from user messages, then queues them for music synthesis via Fal.ai's minimax-music model. It polls asynchronously until the track is ready, delivering lyrics and audio URL back in chat. Crafts original, structured lyrics with verses, choruses, and bridges using OpenAI Submits to Fal.ai for melody, instrumentation, and vocals aligned to the style Handles long-running generations with smart looping and status checks Returns complete song package (lyrics + audio link) for seamless sharing 🔧 Prerequisites n8n account (self-hosted or cloud with chat integration enabled) OpenAI account with API access for GPT models Fal.ai account for AI music generation 🔑 Required Credentials OpenAI API Setup Go to platform.openai.com → API keys (sidebar) Click "Create new secret key" → Name it (e.g., "n8n Songwriter") Copy the key and add to n8n as "OpenAI API" credential type Test by sending a simple chat completion request Fal.ai HTTP Header Auth Setup Sign up at fal.ai → Dashboard → API Keys Generate a new API key → Copy it In n8n, create "HTTP Header Auth" credential: Name="Fal.ai", Header Name="Authorization", Header Value="Key [Your API Key]" Test with a simple GET to their queue endpoint (e.g., /status) ⚙️ Configuration Steps Import the workflow JSON into your n8n instance Assign OpenAI API credentials to the "OpenAI Chat Model" node Assign Fal.ai HTTP Header Auth to the "Generate Music Track", "Check Generation Status", and "Fetch Final Result" nodes Activate the workflow—chat trigger will appear in your n8n chat interface Test by messaging: "Create an upbeat pop song about road trips" 🎯 Use Cases Content Creators: YouTubers generating custom jingles for videos on the fly, streamlining production from idea to audio export Educators: Music teachers using chat prompts to create era-specific folk tunes for classroom discussions, fostering interactive learning Gift Personalization: Friends crafting anniversary R&B tracks from shared memories via quick chats, delivering emotional audio surprises Artist Brainstorming: Songwriters prototyping hip-hop beats in real-time during sessions, accelerating collaboration and iteration ⚠️ Troubleshooting Invalid JSON from AI Agent: Ensure the system prompt stresses valid JSON; test the agent standalone with a sample query Music Generation Fails (401/403): Verify Fal.ai API key has minimax-music access; check usage quotas in dashboard Status Polling Loops Indefinitely: Bump wait time to 45-60s for complex tracks; inspect fal.ai queue logs for bottlenecks Lyrics Under 600 Characters: Tweak agent prompt to enforce fuller structures like [V1][C][V2][B][C]; verify output length in executions

Daniel NkenchoBy Daniel Nkencho
601

Automate invoice processing with OCR, GPT-4 & Salesforce opportunity creation

PDF Invoice Extractor (AI) End-to-end pipeline: Watch Drive ➜ Download PDF ➜ OCR text ➜ AI normalize to JSON ➜ Upsert Buyer (Account) ➜ Create Opportunity ➜ Map Products ➜ Create OLI via Composite API ➜ Archive to OneDrive. --- Node by node (what it does & key setup) 1) Google Drive Trigger Purpose: Fire when a new file appears in a specific Google Drive folder. Key settings: Event: fileCreated Folder ID: google drive folder id Polling: everyMinute Creds: googleDriveOAuth2Api Output: Metadata { id, name, ... } for the new file. --- 2) Download File From Google Purpose: Get the file binary for processing and archiving. Key settings: Operation: download File ID: ={{ $json.id }} Creds: googleDriveOAuth2Api Output: Binary (default key: data) and original metadata. --- 3) Extract from File Purpose: Extract text from PDF (OCR as needed) for AI parsing. Key settings: Operation: pdf OCR: enable for scanned PDFs (in options) Output: JSON with OCR text at {{ $json.text }}. --- 4) Message a model (AI JSON Extractor) Purpose: Convert OCR text into strict normalized JSON array (invoice schema). Key settings: Node: @n8n/n8n-nodes-langchain.openAi Model: gpt-4.1 (or gpt-4.1-mini) Message role: system (the strict prompt; references {{ $json.text }}) jsonOutput: true Creds: openAiApi Output (per item): $.message.content → the parsed JSON (ensure it’s an array). --- 5) Create or update an account (Salesforce) Purpose: Upsert Buyer as Account using an external ID. Key settings: Resource: account Operation: upsert External Id Field: taxid_c External Id Value: ={{ $json.message.content.buyer.tax_id }} Name: ={{ $json.message.content.buyer.name }} Creds: salesforceOAuth2Api Output: Account record (captures Id) for downstream Opportunity. --- 6) Create an opportunity (Salesforce) Purpose: Create Opportunity linked to the Buyer (Account). Key settings: Resource: opportunity Name: ={{ $('Message a model').item.json.message.content.invoice.code }} Close Date: ={{ $('Message a model').item.json.message.content.invoice.issue_date }} Stage: Closed Won Amount: ={{ $('Message a model').item.json.message.content.summary.grand_total }} AccountId: ={{ $json.id }} (from Upsert Account output) Creds: salesforceOAuth2Api Output: Opportunity Id for OLI creation. --- 7) Build SOQL (Code / JS) Purpose: Collect unique product codes from AI JSON and build a SOQL query for PricebookEntry by Pricebook2Id. Key settings: pricebook2Id (hardcoded in script): e.g., 01sxxxxxxxxxxxxxxx Source lines: $('Message a model').first().json.message.content.products Output: { soql, codes } --- 8) Query PricebookEntries (Salesforce) Purpose: Fetch PricebookEntry.Id for each Product2.ProductCode. Key settings: Resource: search Query: ={{ $json.soql }} Creds: salesforceOAuth2Api Output: Items with Id, Product2.ProductCode (used for mapping). --- 9) Code in JavaScript (Build OLI payloads) Purpose: Join lines with PBE results and Opportunity Id ➜ build OpportunityLineItem payloads. Inputs: OpportunityId: ={{ $('Create an opportunity').first().json.id }} Lines: ={{ $('Message a model').first().json.message.content.products }} PBE rows: from previous node items Output: { body: { allOrNone:false, records:[{ OpportunityLineItem... }] } } Notes: Converts discount_total ➜ per-unit if needed (currently commented for standard pricing). Throws on missing PBE mapping or empty lines. --- 10) Create Opportunity Line Items (HTTP Request) Purpose: Bulk create OLIs via Salesforce Composite API. Key settings: Method: POST URL: https://<your-instance>.my.salesforce.com/services/data/v65.0/composite/sobjects Auth: salesforceOAuth2Api (predefined credential) Body (JSON): ={{ $json.body }} Output: Composite API results (per-record statuses). --- 11) Update File to One Drive Purpose: Archive the original PDF in OneDrive. Key settings: Operation: upload File Name: ={{ $json.name }} Parent Folder ID: onedrive folder id Binary Data: true (from the Download node) Creds: microsoftOneDriveOAuth2Api Output: Uploaded file metadata. --- Data flow (wiring) Google Drive Trigger → Download File From Google Download File From Google → Extract from File → Update File to One Drive Extract from File → Message a model Message a model → Create or update an account Create or update an account → Create an opportunity Create an opportunity → Build SOQL Build SOQL → Query PricebookEntries Query PricebookEntries → Code in JavaScript Code in JavaScript → Create Opportunity Line Items --- Quick setup checklist 🔐 Credentials: Connect Google Drive, OneDrive, Salesforce, OpenAI. 📂 IDs: Drive Folder ID (watch) OneDrive Parent Folder ID (archive) Salesforce Pricebook2Id (in the JS SOQL builder) 🧠 AI Prompt: Use the strict system prompt; jsonOutput = true. 🧾 Field mappings: Buyer tax id/name → Account upsert fields Invoice code/date/amount → Opportunity fields Product name must equal your Product2.ProductCode in SF. ✅ Test: Drop a sample PDF → verify: AI returns array JSON only Account/Opportunity created OLI records created PDF archived to OneDrive --- Notes & best practices If PDFs are scans, enable OCR in Extract from File. If AI returns non-JSON, keep “Return only a JSON array” as the last line of the prompt and keep jsonOutput enabled. Consider adding validation on parsing.warnings to gate Salesforce writes. For discounts/taxes in OLI: Standard OLI fields don’t support per-line discount amounts directly; model them in UnitPrice or custom fields. Replace the Composite API URL with your org’s domain or use the Salesforce node’s Bulk Upsert for simplicity.

Le NguyenBy Le Nguyen
942