What Is Sentiment Analysis?
Sentiment analysis is the process of classifying a piece of text as positive, negative, or neutral, and optionally scoring how strongly it leans one way. It is one of the most practically useful applications of AI for businesses.
Applied to real data, sentiment analysis answers questions like:
- Are customers happy with our new product feature?
- Is this support ticket urgent and frustrated, or routine?
- How is our brand perceived on social media this month?
- Which product reviews should we respond to first?
- Is employee survey feedback trending positive or negative?
Manually reading and classifying hundreds or thousands of responses is slow, inconsistent, and expensive. Automated sentiment analysis does the same job at scale, in seconds, at a fraction of the cost.
Why Do It in Google Sheets™?
Most businesses already live in Google Sheets™. Customer feedback from surveys, support tickets from helpdesk exports, review data from scraped CSVs. It all lands in a spreadsheet at some point. The problem is that getting AI analysis out of the spreadsheet, into a tool, and back again is slow and error-prone.
Running sentiment analysis directly inside your spreadsheet means:
- No data export/import cycle. The results appear next to the raw data
- Formulas update when source data changes
- Results can feed other formulas (COUNTIF, SUMIF, pivot tables)
- Your whole team can use it without any training on external tools
How to Do Sentiment Analysis in Google Sheets™ with SheetSense
The SheetSense add-on adds a =AI_SENTIMENT() function that runs AI on any text in your sheet and returns a sentiment label with a confidence score.
The Formula
The output format is Label (Score), so Positive (0.94) means the model is 94% confident the text is positive. This structure is designed to be easy to parse further with other formulas.
What It Looks Like in Practice
| A. Customer Review | B, =AI_SENTIMENT(A2) | C, =AI_KEYWORDS(A2, 3) |
|---|---|---|
| "Absolutely love this product. Fast shipping, works perfectly." | Positive (0.97) | shipping, product, fast |
| "It's fine. Does what it says but nothing special." | Neutral (0.71) | fine, special, works |
| "Terrible customer service. Waited 3 weeks for a refund." | Negative (0.96) | refund, wait, customer service |
| "Great value for the price. Minor issue with setup." | Positive (0.68) | value, price, setup |
| "Doesn't work at all. Complete waste of money." | Negative (0.98) | waste, work, money |
Once column B is filled with =AI_SENTIMENT(A2) dragged down, you can run a COUNTIF to count positive/negative/neutral responses, build a chart, or filter to see only negative reviews that need a response.
Step-by-Step Setup
Install SheetSense
Go to Extensions → Add-ons → Get add-ons. Search "SheetSense" and install. Accept the permissions (only requires access to the current spreadsheet).
Put Your Text Data in Column A
Paste or import your customer reviews, survey responses, support tickets, or social media comments. One piece of text per row.
Type the Formula in B2
Click cell B2. Type: =AI_SENTIMENT(A2). Press Enter. Wait 2-3 seconds for the first result to appear.
Drag Down to Fill the Column
Click B2, then drag the small blue square in the bottom-right corner down to fill the formula for all your rows. SheetSense processes each row and caches results, repeated runs on the same text are instant.
Analyze the Results
Use a COUNTIF formula to count each sentiment: =COUNTIF(B:B, "Positive*"). Build a pie chart. Filter column B to show only "Negative" rows. Pivot table by sentiment label. The data is yours to explore.
Free Template: Try the SheetSense Playground demo sheet to see =AI_SENTIMENT() and other functions in action before installing. View it at: SheetSense Playground →
Tool Comparison: SheetSense vs. Enterprise Solutions
SheetSense is not the only option for sentiment analysis. Here is an honest comparison of three common tools, including two used by large enterprise teams.
Pricing Comparison
| Feature | SheetSense | Brandwatch | MonkeyLearn |
|---|---|---|---|
| Starting price | Free | ~$800/mo | $299/mo |
| Setup time | 60 seconds | Days (onboarding) | Hours-days |
| Works in Google Sheets | Yes (native) | Export required | API/export |
| Confidence scores | Yes | Yes | Yes |
| Handles sarcasm | Yes (AI) | Yes | Depends on model |
| Social media monitoring | No | Yes (core feature) | Limited |
| Custom model training | No | Yes (enterprise) | Yes |
| Best for | Spreadsheet-based analysis | Enterprise brand monitoring | Custom ML workflows |
When to Use Each Tool
Use SheetSense when: Your text data is already in (or comes into) Google Sheets. You need to analyze customer reviews, survey responses, support tickets, or any batch of text without building a data pipeline. It covers 95% of real-world sentiment analysis needs for a fraction of enterprise tool pricing.
Use Brandwatch when: You need real-time monitoring of brand mentions across the web, you have a dedicated social media or PR team, and your budget supports $800+/month for brand intelligence. It is a genuinely excellent product for enterprise use cases.
Use MonkeyLearn when: You need to train a custom sentiment model on domain-specific language (medical, legal, highly technical), you have labeled training data, and you need to connect to external tools like Zendesk automatically. The platform shines for teams doing continuous, high-volume analysis in a specific domain.
Try Sentiment Analysis in Your Sheet: Free
Install SheetSense and type =AI_SENTIMENT(A1) in any cell. 50 free calls per month, no credit card required.
Related articles: • How to Use AI in Google Sheets • Best Google Sheets Add-Ons 2026