Analyzing text data in a spreadsheet is a nightmare. It’s time-consuming and monotonous, not to mention expensive.
Open-ended responses that you’ve collected from customer surveys, for example, can be incredibly difficult to process, especially if you receive a high volume of responses.
Should you read the answers and tag them one by one? What if you want to know what people are saying about a particular topic? It’s impossible to deliver accurate results on time.
To solve this problem, text analysis with AI is here to help.
Automatically analyze responses to customer surveys, social media comments, or product reviews in Google Sheets. Just connect MonkeyLearn’s text analysis add-on and start gaining insights in next to no time.
In this guide, learn how text analysis can speed up your processes and how to make the most out of no-code text analysis tools.
Text analysis uses machine learning and natural language processing to make sense of large amounts of data and automatically sort it.
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.
Text analysis models first need to be trained to understand the human language. But don’t fret, it’s not difficult at all. You’ll just need some tagged samples of your data. Once text analysis tools have been trained with enough samples, they’ll start making predictions on their own.
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:
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.
If you want to get started ASAP, you can use MonkeyLearn – a machine learning platform with a suite of text analysis tools and a Google Sheets text analysis Add-on
To start analyzing the data in your Google Sheets, you can use one of the pre-trained models. Since they have already been trained, you just need to choose the type of text analysis you want to run 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.
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 API 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.
You may use pre-trained models, like the ones above, to analyze your data in Google Sheets. But you also have the option to create your own. Perhaps you need your model to recognize industry jargon. By using a custom model, you’ll get more accurate insights.
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 to create a new 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.
You now have all the info you need to start analyzing texts in Google Sheets, and save countless hours of tedious work. Read on to see how artificial intelligence for Google Sheets is helping many businesses today.
Public reviews are becoming increasingly important: more than 80% of buyers report they would never buy something before reading comments online), and brands can gain incredible insights from them.
Luckily, you can perform sentiment analysis on product reviews and other customer data to quickly understand if customers like your product or service. For example, we were able to understand how customers feel about hotels worldwide from a series of TripAdvisor reviews.
Below, you can see that , London received the most negative reviews when compared to other major cities:
Customers voice their opinion about your brand all the time, on Twitter, Facebook, in online reviews, surveys, and more. And text analysis can help figure whether overall brand perception is positive or negative. It’s a good idea to monitor brand sentiment over time, so you can spot any major drops or fluctuations in positive or negative comments, and quickly assess whether you have a problem or you’re gaining popularity.
Doing market research is crucial 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.
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. To be able to analyze them efficiently, you’ll need to implement text analysis with AI.
Analyze information in Google Sheets by integrating MonkeyLearn – a text analysis tool that’s scalable, consistent, and analyzes data in real-time.
Request a demo to see how you can perform text analysis in Google Sheets and get the most out of your unstructured data.
August 6th, 2019