Analyze Data in Google Sheets with Text Analysis

Analyzing data in a spreadsheet can be a nightmare. It’s time-consuming and monotonous. 

Customer survey data that you’ve collected, for example, can be incredibly difficult to process, even after you’ve added all the results to a spreadsheet, especially if you receive a high volume of responses. 

How do you process this information? Should you read the answers one by one? What if you want to know what people are saying about your brand on social media? It’s impossible for teams to read rows and rows of comments and survey responses in a spreadsheet, and deliver accurate results on time. In addition, this is tedious work and boredom can lead to inconsistencies. 

To solve this problem, text analysis with AI is here to help. 

If you have responses to customer surveys, social media comments, or product reviews in Google Sheets, you can automatically analyze this data in that very same spreadsheet. Not only is it easy to use our AI add-on for Google, for text analysis, it will save you precious time and resources.   

In this guide, we are going to take a look at how you can carry out text analysis in Google Sheets using AI, including:

Let’s get going!

What Is Text Analysis with AI? 

Text analysis is a machine learning technique that allows you to sort large amounts of data using extraction and classification models. An extractor pulls out important information from data in a spreadsheet, while a classifier sorts data into categories. Some examples of texts that a company might want to analyze in a spreadsheet are Facebook posts, emails, product reviews, NPS responses, and more.

In addition, manually processing data is expensive for businesses, especially for large ones. Imagine how much time (and money!) a company like Zoom would have to spend analyzing thousands of reviews every day.

Thanks to text analysis, businesses can carry out analysis tasks much more efficiently. They’re also able to gain valuable insights that human agents might miss. 

Now, for natural language processing algorithms to be able to derive meaning from your text, you need to train them. But don’t fret! It’s not difficult at all. The most crucial thing you’ll need is tagged samples of data. Once the machine is trained with enough samples, it can start associating data and differentiating pieces of text from one another.

How is this process useful for a company? Well, this technique comes in handy when trying to figure out the sentiment behind a product review, the language of a certain email, or if an issue needs an urgent answer. Let’s take a look at some examples in which AI helps examine text within spreadsheets.

How Is Text Analysis in Google Sheets Useful? 

Text analysis tools can analyze data in Google Sheets in seconds, and gain interesting insights that can help your business make data-driven decisions. Let’s take a look at some of the benefits of text analysis in more detail:

  • It’s scalable. To manually analyze data in Google Sheets, a person would have to read each row and try to make sense of all the information. It could take hours or even days, and the task becomes especially tedious if it has to be done every day! Text analysis can scale down these hours to minutes, or even seconds, so you and your team can spend time on more fulfilling tasks.
  • Analyzes in real-time. You have less than 60 minutes to prevent negative comments from turning into a crisis. Is there something you can do about it? Sure! Text analysis can detect these issues in real-time by spotting topics such as Problem, Bug, and Slow and classifying them by sentiment. For example, you’ll want to prioritize comments that are classified as Negative and Urgent.
  • It’s consistent. People naturally have different opinions, feelings, and points of view. Let’s imagine your team needs to tag product reviews in a spreadsheet to understand if they are positive or negative. One agent may think that a comment such as “I absolutely loved the camera, though the pricing was a bit high” is a great review and tag it as Positive. However, another member of your team may tag it as Negative because of the ‘high pricing’ comment. 

    Human error and inconsistencies mean that your insights are not as accurate as they could be. How can you prevent this from happening? Text analysis analyzes text using one set of rules. When you train a model, you teach it to follow a set of criteria that doesn’t change.

After analyzing why text analysis is useful, it’s time to look at how to analyze your text in Google Sheets.

How to Use Text Analysis in Google Sheets

If you want to get started ASAP, you can use MonkeyLearn! We are a machine learning platform for text analysis using AI that will allow you to get valuable insights from raw text. 

To start analyzing the data in your Google Sheets, you can use one of our pre-trained models. As they have already been trained, you just have to run this model to analyze your data in Google Sheets, and AI will do the rest!

Follow these steps: 

1- Install MonkeyLearn’s add-on for Google Sheets

After creating a free account on MonkeyLearn, go to the Google Sheets Add-ons landing page. You’ll see a screen like this one: 

MonkeyLearn will need your permission to run:

And ask for the following permissions:

Finally, you have to connect the MonkeyLearn API to your Google Sheets. First, go to ‘Add-ons’. Then, click on MonkeyLearn —> Start. You’ll see a menu to the right. There, you’ll have to set up the API key. Click on the link, which will take you to MonkeyLearn’s page. Just copy and paste the API key, and you are ready to go!

2- Decide on a Model to Use

On MonkeyLearn’s dashboard, you will find different options: a sentiment analysis model, a keyword extractor, an urgency detection model, and more. Choose the one that suits your needs. In this guide, we are going to show you how to use the keyword extractor in detail, but the process is the same for all of the models.

In MonkeyLearn’s add-on for Google Sheets, you’ll see a window on the right-hand side of the screen where you’ll be able to select the model you want to use. In this case, we chose the Keyword Extractor:

3- Run the Analysis and See the Results!

The only thing left to do to analyze data automatically in Google Sheets is to specify the Column or Range of the pieces you want to examine with the model. In this case, we have reviews that go from cell B2 to cell B7. So, you need to add B2: B7 in the Column or Range field:

After this, just click ‘Run’ and that’s it! The model will take a look at all our product reviews and give us the keywords in column C:

And that’s it! Quite simple, right? You can follow the same steps for any of our pre-trained text analysis models available in your dashboard.

Create Your Own Machine Learning Model

You may use pre-trained models, like the ones above, to analyze your data in Google Sheets. But you also have the option of adapting models to the specific requirements of your business. For example, you can add categories to the sentiment analysis model to see if your customers are angry or happy. You can also train your model to recognize industry jargon. By using a custom model, you’ll get more accurate insights than if you used a pre-trained model.  

Training a text classifier or extractor with machine learning is really simple with MonkeyLearn. Follow this tutorial to create your own topic classification model.

1- Create Your Own Model

Sign in to your account and access the dashboard. Then, click on the button ‘Create model’:

This time, let’s create a classifier:

2- Upload Your Texts

In this step, you’ll need to upload the information you want to analyze. You can upload information in various formats, including CSV and Excel files: 

3- Choose Your Tags

Now it’s time to choose your tags. In this case, we went for Ease of Use, Customer Service, and Pricing. These are the tags that the model will later on use to categorize data and make predictions. Keep in mind that if you decide to use many tags, you will need further work to train the model:

4- Start Training Your Classifier

It’s time to train your model so it can recognize the information you want to classify. For that, you will have to manually tag some of the pieces of text you uploaded, as shown below:

5- Test it

After tagging a certain number of samples, you’ll be ready to test your model. You have to give your model a name, and click ‘Test’:

Then, type something in the box and see how it works:

In this case, it worked very well! 

6- Use the Machine Learning Model in Google Sheets

If you want to use this custom model in Google Sheets, then you need to follow certain steps. As shown below, you have to access the sheet with the information you want to analyze. Then, you have to go to ‘Add-ons’ in the toolbar and choose your custom model. 

Finally, decide the columns and rows you want to analyze and your model will show you the results!

If your model is not as accurate as you’d like, don’t worry, you can go back and train it by tagging more data to make it smarter. 

Want to do the same with an extractor? Follow this guide.

Use Cases and Applications

You now have all the info you need to start analyzing texts in Google Sheets and save countless hours of tedious work. Now, how is this helpful for your business? Well, it depends on your goals and objectives. Read on to see how artificial intelligence for Google Sheets is helping many businesses today.

Get Insights into Customer Feedback

Public reviews are becoming increasingly important. They are crucial for clients (more than 80% of buyers report they would never buy something before reading comments online), and for brands, too, as they give incredible insights into the overall customer experience.

Let’s picture this situation: you want to know how much your clients like your new product, so you ask them to fill in a survey. You end up getting thousands of reviews and compiling them into a spreadsheet… and that’s far more than you expected. Now you have to derive meaning out of this pile of reviews sitting in your Google Sheets. Luckily, text analysis can help you sort them quickly and easily. 

You can use this sentiment analysis model to understand if customers liked your product or not. For example, we ran a sentiment analysis model to analyze TripAdvisor reviews and figure out how people felt about hotels in different cities around the world. As you can see from the following graphic, London received the worst reviews when compared to other major cities:

Sentiment of hotel reviews across the different cities

Understand Your Brand Perception

Today, there is a lot of information online about what people think about your brand. You can read tweets, Facebook reviews, blog articles, Amazon reviews, comments in the Google Play Store: the possibilities are endless. Text analysis can help figure out how your brand is perceived online. 

In 2016, the UK voted to abandon the European Union, a process that would be known as Brexit. At the time, we wanted to know what people were saying about this historical event. That’s why we gathered more than 250,000 tweets in English and analyzed them with our sentiment analysis model. This helped us find out if the tweets were in favor, against, or neutral about Brexit.

We also wanted to quickly understand which aspects people were discussing, so we analyzed the tweets with a keyword extraction model. This way, we obtained the main words and phrases that people use when referring to this event. 

Not surprisingly, the opinion was hugely divided:

Most relevant keywords for positive tweets with #brexit.

Those in favor used the following expressions: 

Those against Brexit used these expressions: 

Applying these text classification and extraction models was quite simple. You can do the same with your business, to analyze what people on social media are saying about your brand, make informed decisions based on that information, and step up your game.

Do Valuable Market Research

Doing market research is a crucial activity for any business. Would you like to know more about your target audience? Is spotting a new business opportunity one of your objectives? It’s particularly useful to get insights on problematic areas and to identify unaddressed customer needs. 

One way of applying machine learning to this process is to examine product reviews, social media comments, articles, or survey responses that you’ve collected in a spreadsheet. Using an extractor for keyword analysis, for example, can save you a lot of time and effort when taking a look at these texts. It’s a lot faster than reading every review yourself and you won’t have to spend hours creating filters or complex formulas in spreadsheets to examine your data. Instead, this keyword extractor will organize all the information with simple, easy-to-read tags.

When running such a model, you may discover certain topics your target clients are discussing, and come up with a great idea to solve their issues or continue doing what you’re doing if they’re mentioning topics in a positive light. 

For example, we analyzed Slack’s reviews in Capterra. We ran an aspect-based sentiment analysis and received the following insights:

As you can see, Slacks perform well in a lot of aspects (such as Ease of Use, Integrations or Groups). However, people complain a lot about their Notifications, Pricing, and Performance-Quality-Reliability. This analysis gives the Slack team crucial information on aspects to improve their service. 

Outselling competitors is also possible when applying AI to your Google Sheets. You may realize that many customers are complaining about a certain feature in your competitor’s product, an insight that you can use to your advantage by improving that particular feature in your own product.


We all know that analyzing texts in a spreadsheet can be a hassle if you have large volumes of data to work with. It’s tedious, boring, and expensive. When somebody buys a product, they will often write a review, which leads to new information. Now imagine that you’re receiving hundreds or even thousands of those reviews. 

The good news is that your business can take advantage of all the insights contained in these reviews, emails, and survey responses. However, to be able to analyze them efficiently, you’ll need to implement text analysis with AI.

It’s very easy to analyze information in Google Sheets by integrating MonkeyLearn. Forget about manually tagging rows of text – text analysis is scalable, works in real-time, and always follows the same criteria, making it faster, more efficient, and accurate than humans. 

The only thing you have to do is to choose a suitable pre-trained model to process your texts. Or, you can train your own model, which is just as easy. Even if it takes a little bit more time to train your custom model than using a ready-made one, it will give you results that are tailored to your business. 

Don’t hesitate to ask for a demo. Our team will help you to carry out text analysis in Google Sheets and show you how to use this technology in your company.

Federico Pascual

Federico Pascual

COO & Co-Founder @MonkeyLearn. Machine Learning. @500startups B14. @Galvanize SoMa. TEDxDurazno Speaker. Wannabe musician and traveler.


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