AI Sentiment Analysis with Make.com

This guide shows you how to build a complete AI sentiment analysis workflow in make.com, from capturing text to routing results, using visual no-code tools and AI models.

By the end, you will know how to connect your data sources, call an AI model for classification, and send sentiment tags to your CRM, help desk, or notification channels.

What is AI sentiment analysis in make.com?

AI sentiment analysis is the process of using language models to determine whether a piece of text is positive, negative, or neutral. In make.com you can automate this process by creating a scenario that receives text, sends it to an AI service, and stores or routes the result.

Typical use cases include:

• Analyzing customer support tickets in real time
• Tagging survey responses by emotional tone
• Prioritizing negative feedback for faster handling
• Summarizing and scoring social media mentions

Instead of manually reading every message, make.com lets you design one automated flow and reuse it across channels.

Prerequisites for building the workflow in make.com

Before you create the scenario, prepare the following:

• A make.com account with access to the visual scenario builder
• Access to a data source, such as email, forms, a chat tool, or a CRM
• An AI provider connection (for example, OpenAI or a compatible LLM API)
• Destination apps where you want to store or route sentiment labels

You should also have a basic idea of the types of text you will analyze and how you want to classify sentiment, such as a three-level or five-level scale.

Planning your sentiment analysis workflow in make.com

Good planning makes the automation easier to maintain. Define:

• Input text: Where the text comes from (email body, form field, chat message, survey answer).
• Sentiment scale: For example, positive, negative, neutral; or a 1–5 satisfaction scale.
• Actions by category: What happens if sentiment is negative, positive, or neutral.
• Storage: Where you will store labels and original text for later reporting.

Once these elements are clear, you can translate them into modules and routes in a make.com scenario.

Step-by-step: Build an AI sentiment scenario in make.com

Follow these steps to create an end-to-end automation.

Step 1: Create a new scenario in make.com

1. Log in to your make.com dashboard.
2. Click Create a new scenario.
3. On the canvas, choose the app that will provide the text, for example:

• Email
• Google Forms or another form builder
• Help desk software
• Chat or messaging tools

Set the first module as a trigger so new messages automatically start the workflow.

Step 2: Normalize and prepare the input text

To get consistent AI results, clean the incoming data. In the scenario editor:

1. Add a Text aggregator or similar module if the message has multiple parts.
2. Use text functions to trim whitespace or remove signatures if needed.
3. Map the final cleaned text to an output variable, for example clean_text.

This ensures the AI model in make.com receives focused content, which improves classification accuracy.

Step 3: Connect an AI module in make.com

Next, add the module that sends text to your AI provider:

1. Click the plus icon to add another module.
2. Select your AI or LLM app connection.
3. Choose a model suitable for classification or general text understanding.
4. In the prompt or input field, insert the mapped clean_text.

Design a clear prompt, for example:

• “Classify the sentiment of the following text as Positive, Negative, or Neutral. Return only one word: Positive, Negative, or Neutral.”

Keep prompts short and deterministic so the output is easy to parse in later routes.

Step 4: Parse the AI sentiment output

After the AI module runs, you will receive a sentiment label in the response body. In make.com you can:

1. Map the AI response text to a new variable named sentiment.
2. Add a Router module to split the flow based on conditions.
3. Create routes for each possible sentiment value.

For example:

• Route 1: sentiment = "Positive"
• Route 2: sentiment = "Negative"
• Route 3: sentiment = "Neutral"

Using clear conditions lets make.com direct messages to the correct action automatically.

Step 5: Store sentiment results in your systems

Decide where to persist the analysis. Common options include:

• Adding custom fields in a CRM record
• Appending columns in a spreadsheet
• Updating a help desk ticket with sentiment tags
• Logging to a database for reporting

On each route in make.com, add modules such as:

• “Update record” in your CRM
• “Add a row” in Google Sheets or a database
• “Add a tag” or “Set priority” in your ticketing tool

Map both the original text and the sentiment label so you can analyze trends over time.

Step 6: Trigger follow-up actions automatically

Once the sentiment is stored, you can trigger different automations for each category. For example:

• Negative: Create a task for a manager, send an alert message, or escalate the ticket.
• Positive: Send a thank-you email or invite the customer to leave a public review.
• Neutral: Archive for reporting or send a clarification survey.

In make.com, configure these as additional modules on each router branch, such as sending email, posting to a channel, or creating tasks in a project tool.

Optimizing AI sentiment performance in make.com

To keep the workflow accurate and efficient:

• Refine prompts: Test several prompt variations to get clean, single-word outputs.
• Limit text length: Truncate extremely long messages to keep processing fast and focused.
• Log edge cases: Store samples where the model is unsure and review them manually.
• Iterate: Adjust routing conditions or sentiment labels as you learn more from real data.

Run the scenario in test mode to review outputs before turning it on for production traffic.

Examples of sentiment workflows built on make.com

Here are a few practical patterns you can adapt:

• Support triage: Analyze every new ticket, increase priority for negative sentiment, and ping a specialist channel.
• Survey analysis: Add a module that classifies each open-text response, then build reports grouped by sentiment.
• Social monitoring: Ingest mentions from social platforms, classify, and send negative posts into a dedicated response queue.
• Product feedback: Store feedback sentiment linked to features for roadmap decision-making.

Each of these ideas can be built by combining the same core pattern in make.com: trigger, AI classification, routing, storage, and action.

Further resources for using make.com with AI

To go deeper into AI automation concepts, you can explore additional scenario design tutorials and best practices from analytics or automation specialists, such as the materials provided by Consultevo.

For detailed reference on the original tutorial and additional configuration screenshots, visit the official how-to guide on AI sentiment workflows at make.com AI sentiment analysis.

With these steps and examples, you can confidently deploy AI-powered sentiment analysis in make.com to turn raw text into actionable insight across your customer journeys.

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