As you probably already know, Excel spreadsheets are one of the most extended ways of working with big collections of data. They are powerful and easy to combine and integrate with a myriad of other tools. Through our Excel Add-in, we enable you to add MeaningCloud’s analysis capabilities to your work pipeline. The process is very simple as you do not need to write any code.
In this tutorial, we are going to show you how to use our Excel Add-in to perform text classification. We are going to do so by analyzing restaurant reviews we’ve extracted from Yelp. If you have already read some of our previous tutorials, this first part may sound familiar.
To get started, you need to register in MeaningCloud (if you haven’t already), and download and install the Excel add-in on your computer. Here you can read a detailed step by step guide to the process.
Once you’ve installed it, a new tab called MeaningCloud will appear when you open Excel. If you click on it, you will see the following buttons:
To start using the add-in, you need to copy your license key and paste it into the corresponding field in the Settings menu. You are required to do this only the first time you use the add-in, so if you have already used it, you can skip this step.
Once the license key is saved, you are ready to start analyzing!
As mentioned above, we are going to analyze restaurant reviews extracted from Yelp, more specifically reviews on restaurants in London. You can download the spreadsheet we are going to use here and follow the tutorial as you read.
You will see that after opening the spreadsheet with your data in Excel, you will be able to analyze them in just a few clicks.
The results will be output in a new sheet, so it is important not to lose each review’s reference to its correspondent restaurant. To do this, we will use the second field in the “Input” section: the IDs.
Click on the checkbox to enable it and then select a cell of the column you want to move in association with the texts we are going to analyze. In this case, it can be any cell of column A.
The image on the left shows how the “Input” section looks after selecting the data as described.
Step 3: configure the analysis
The second section, “Analysis Settings” features two configurable fields: the language in which we are going to analyze the text and the classification model we are going to use.
If we take a look at the categories of each one of these models, we can see that both IPTC and SocialMedia do not have specific categories for food or restaurants. The closest ones in both cases are the categories related to “lifestyle and leisure“, but this does not give us much additional information for our scenario.
On the other hand, the IAB model has a Tier 1 category called Food & Drink, which includes some subcategories related precisely to the domain we want to analyze. It seems clear that this is the best model for our analysis out of the three provided.
Once we have set all the desired values, the only thing left to do is to click on the button “Analyze” to start the analysis.
When you do this, a progress bar will appear to show you the progress of the analysis. In the background, you will be able to see how a new sheet with the results is created and how the values are written as they are received from the API.
When the process finishes, your excel spreadsheet will have a new sheet called Text Classification with the result of the classification according to the selected model. You can read more about the output and how to configure it in the text classification in excel documentation. For this tutorial, we’ve used the default configuration, which shows in the output all the possible fields and selects just one category per text analyzed.
Step 4: analyze the results
You can download the spreadsheet with the results, the analysis and the pivot chart here.
But what can you do if the predefined classification models provided are not relevant to what you want to classify or do not provide enough detail? Stay tuned for our next tutorial, in which we will show you how to define with MeaningCloud your own classification model and use it in Excel.
And, of course, if you have any questions, we’ll be happy to answer them at firstname.lastname@example.org