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Analyze Google Forms feedback with Gemini AI and send weekly reports
https://n8nworkflows.xyz/workflows/analyze-google-forms-feedback-with-gemini-ai-and-send-weekly-reports-13772
# Analyze Google Forms feedback with Gemini AI and send weekly reports
This document provides a technical breakdown of the **Customer Feedback Analyzer** workflow, designed to automate the sentiment analysis and reporting of Google Forms responses using Gemini AI.
---
### 1. Workflow Overview
The workflow automates the transition from raw customer feedback to structured business intelligence. It monitors a Google Sheet (linked to a Google Form), applies generative AI to categorize and score the text, and distributes the findings across project management tools and email.
**Logical Blocks:**
* **1.1 Data Collection:** Triggered on a schedule to fetch and filter new, unanalyzed responses.
* **1.2 AI Intelligence:** Uses Google Gemini to perform multi-dimensional analysis (sentiment, theme, and recommendations).
* **1.3 Data Persistence & Reporting:** Updates the source spreadsheet, logs high-level metrics in Notion, and sends a formatted HTML report via Gmail.
---
### 2. Block-by-Block Analysis
#### 2.1 Data Collection
**Overview:** This block acts as the entry point, ensuring only new data is processed to manage API costs and prevent duplicate analysis.
* **Nodes Involved:** `Daily feedback check`, `Read feedback responses`, `Filter unprocessed responses`.
* **Node Details:**
* **Daily feedback check (Schedule Trigger):** Configured to trigger every day at 9:00 AM.
* **Read feedback responses (Google Sheets):** Connects to the specific Document ID and Sheet Name where Google Form responses are stored.
* **Filter unprocessed responses (Code):**
* **Logic:** Iterates through rows. If the "Processed" column is empty or "no", it adds the row to an array.
* **Output:** It aggregates all individual responses into a single string (`feedbackText`) to allow the AI to process the entire batch in one call.
* **Edge Case:** If no new responses are found, it returns a `skip: true` flag to prevent downstream errors.
#### 2.2 AI Intelligence
**Overview:** Transforms unstructured text into structured JSON data.
* **Nodes Involved:** `Analyze feedback with Gemini`, `Gemini Chat Model`.
* **Node Details:**
* **Analyze feedback with Gemini (Basic LLM Chain):**
* **Configuration:** Uses a system prompt requiring the AI to return a specific JSON schema containing `sentiment`, `score` (1-10), `category`, and `theme`.
* **Input:** Uses the `{{ $json.feedbackText }}` variable from the previous block.
* **Gemini Chat Model (Chat Google Gemini):**
* **Model:** `gemini-2.0-flash`.
* **Settings:** Temperature is set to `0.3` for consistent, deterministic outputs.
* **Failure Type:** API Authentication errors or context window limits if the feedback batch is excessively large.
#### 2.3 Reporting & Distribution
**Overview:** Parses the AI's response and updates external systems.
* **Nodes Involved:** `Parse results and build report`, `Update feedback sheet with results`, `Log insights to Notion`, `Email report to team`.
* **Node Details:**
* **Parse results and build report (Code):**
* **Technical Role:** Uses Regex (`/\{[\s\S]*\}/`) to extract the JSON object from the AI's text response.
* **Logic:** Calculates the `positiveRate` and `negativeRate` and constructs a multi-section HTML string for the email body.
* **Update feedback sheet with results (Google Sheets):**
* **Operation:** `Update`. Maps the AI results back to the original rows using the `rowIndex`.
* **Log insights to Notion (Notion):**
* **Action:** Creates a database page.
* **Properties:** Maps `Total Responses`, `Positive Rate`, `Top Themes`, and `Recommendation`.
* **Email report to team (Gmail):**
* **Configuration:** Sends the generated `emailBody` as an HTML message.
* **Subject:** Dynamic subject line including the current ISO date.
---
### 3. Summary Table
| Node Name | Node Type | Functional Role | Input Node(s) | Output Node(s) | Sticky Note |
| :--- | :--- | :--- | :--- | :--- | :--- |
| Daily feedback check | Schedule Trigger | Workflow Entry | (None) | Read feedback responses | Data collection |
| Read feedback responses | Google Sheets | Data Retrieval | Daily feedback check | Filter unprocessed responses | Data collection |
| Filter unprocessed responses | Code | Data Filtering | Read feedback responses | Analyze feedback with Gemini | Data collection |
| Analyze feedback with Gemini | Basic LLM Chain | AI Processing | Filter unprocessed responses | Parse results and build report | AI analysis |
| Gemini Chat Model | Google Gemini | Language Model | (None) | Analyze feedback with Gemini | AI analysis |
| Parse results and build report | Code | Data Formatting | Analyze feedback with Gemini | Update feedback sheet, Log to Notion | Reporting |
| Update feedback sheet with results | Google Sheets | Data Persistence | Parse results and build report | Email report to team | Reporting |
| Log insights to Notion | Notion | Logging | Parse results and build report | (None) | Reporting |
| Email report to team | Gmail | Communication | Update feedback sheet | (None) | Reporting |
---
### 4. Reproducing the Workflow from Scratch
1. **Preparation:**
* Create a Google Sheet with headers: `Timestamp`, `Feedback`, `Processed`, `Sentiment`, `Category`, `Score`, `Theme`.
* Create a Notion Database with properties: `Date` (Title), `Total Responses` (Number), `Positive Rate` (Number), `Top Themes` (Text), `Recommendation` (Text).
2. **Trigger Setup:** Add a **Schedule Trigger** node set to "Daily".
3. **Data Fetching:** Add a **Google Sheets** node. Set the action to "Get Many" and point it to your feedback sheet.
4. **Batch Processing Logic:** Add a **Code** node. Paste logic to check if `row.Processed` is falsy. Create an object that combines the row index and the text response.
5. **AI Integration:**
* Add a **Basic LLM Chain** node.
* Connect a **Google Gemini Chat Model** node to its AI Model input. Select `gemini-2.0-flash`.
* In the Chain node, write a prompt asking for JSON output including: sentiment, score, and a summary recommendation.
6. **Data Parsing:** Add a **Code** node. Use `JSON.parse` on the AI output. Construct an HTML string using standard tags (`<h2>`, `<ul>`, `<li>`) for the email body.
7. **Update Source:** Add another **Google Sheets** node. Set the action to "Update". Use the `rowIndex` from the AI output to write the sentiment and category back to the specific sheet row. Set "Processed" to "yes".
8. **Logging:** Add a **Notion** node. Set action to "Create Database Page" and map the summary fields.
9. **Notification:** Add a **Gmail** node. Set the "Message" field to the `emailBody` variable and enable the "Is HTML" option.
10. **Credentials:** Ensure OAuth2 credentials are provided for Google (Sheets/Gmail) and API keys for Notion and Google Gemini.
---
### 5. General Notes & Resources
| Note Content | Context or Link |
| :--- | :--- |
| **Prerequisite:** Create a Google Form and link it to a Google Sheet first. | Implementation Requirement |
| **Model Choice:** Gemini 2.0 Flash is recommended for speed and cost-efficiency in text analysis. | Technical Optimization |
| **Scaling:** For high-volume forms, consider increasing the memory limit of the n8n instance. | Infrastructure Note |