Customized Text Classification for Excel
In the previous tutorial we published about Text Classification and MeaningCloud’s Excel add-in, we showed you step by step how to carry out an automatic text classification using an example spreadsheet.
In this tutorial, we are going a bit further: instead of just using one of the predefined classification models we provide, we are going to create our own model using the model customization console in order to classify according to whichever categories we want.
We are going to work with the same example as before: London restaurants reviews extracted from Yelp. We will use some data from the previous tutorial, but for this one we need more texts, so we’ve added some. You can download the spreadsheet here if you want to follow the tutorial along.
If you followed the previous tutorial, you might remember that we tried to use the IAB model (a predefined model for contextual advertisement) to classify the different restaurant reviews and find out what type of restaurants they were. We had limited success: we did obtain a restaurant type for some of them, but for the rest we just got a general category, “Food & Drink“, which didn’t tell us anything new.
This is where our customization tools come in. Our classification models customization console allows you to create a model with the categories you want and lets you define exactly the criteria to use in the classification.
So how do we create this user model?
To create a category, we need to determine two things: its name (or label) and the code associated to it (its univocal identification within the model). In the following image, you can see how we are going to create the category for “Asian food“:
1.3 Add the classification criteria
To set the criteria for a model we have three possibilities: using training texts (statistical model), using rules (rule-based model) or using both combined (hybrid model). You can find some tips on which one to use in the documentation. For our scenario, we are going to use a hybrid model: we have lots of example texts, and it seems easy to add specific terminology for each one of the categories to improve the results.
The spreadsheet available to download at the beginning of the tutorial features several sets of texts that we are going to use to train and evaluate the model. There are four different sheets: TrainingA, Training B, TestA, and TestB. TrainingA is the same set of texts we classified in the previous tutorial, and we are going to use it as the first training text of the model.
To add training text to a category, just access the category view and copy the texts in the section at the bottom, the Training text section. This is what the categories look like after adding the texts from TrainingA:
If you do not want to define the categories yourself at this time, you can download this file and import it directly into your empty model using the default configuration that appears in the import dialog.
Step 2: Validate your model
2.2 Evaluate your model
Thanks to being able to classify texts with the Excel add-in, the process of evaluating our model becomes very easy.
We will carry out an iterative process to analyze some texts and then evaluate the performance of our model. This information will serve us as feedback until we reach a satisfactory performance level. With this in mind, we are going to evaluate the model we trained with the TrainingA set using the texts contained in the Test set.
If we analyze the texts selecting the column with the manual tag as the ‘ID‘, we will obtain a new sheet where we can directly compare the result and the manual tag.
To obtain a quantitative measurement of this comparison, we can easily add a column to the results where to output “1” when the result given is correct (that is, equal to the value of the first column), and “0” when it isn’t.
We can easily do this with the “IF” function. For the first text in the results the formula would be the following: IF(E2=A2;1;0)
If we drag this formula down the column and apply it to all our results, we can sum them at the bottom, and obtain how many are correct.
We get 23 correct out of 42, roughly a 54%, which seems to indicate we need to keep training our model. We can also create a chart such as the one on the right, to see the results by category and identify which ones are more problematic.
The configuration we’ve used to insert this pivot bar chart is the following:
- In the Axis fields area, we’ve added the field ID.
- In the Legend fields area, the field Result, which is the additional column we have created to compare the result to the manual tag.
- In the Values area, the field Result configured as “Count of Result”.
Step 3: Optimize your model
3.1 Optimize using training text
The first thing we are going to do to improve these results is to add more training texts to the model. We are going to add the texts from the TrainingB set — the ones we’ve just used to evaluate — in the same way we added the ones from TrainingA in Step 1.3.
On the right, we’ve included a new chart where we can see that in this new evaluation the results have improved. In this case, 33 out of 42 are correct, around 79%, but we can do better.
You may have noticed that, until now, we’ve only trained the model using training texts, which means that we have not used a hybrid model yet, but a statistical one (you can download this file with the updated model, and import it directly into your empty model using the default configuration that appears in the import dialog).
When we check the texts for which the model has not provided the correct answer, we see that most of them could be easily fixed by adding rules to the categories (for instance, adding “American style” as a marker for a review that should be classified as “American food“), so that’s what we are going to do next.
3.2 Optimize using rules
There are four types of rules we can define for a category: positive terms (mandatory terms that have to appear in the text in order to classify it into a category), negative terms (which exclude a text that contains any of them from a category), relevant terms (add weight for that category), and irrelevant terms (decrease weight for that category).
In this case, we are going to use only relevant and irrelevant terms as positive and negative terms are much more restrictive. This is an example of the rules added to the “American food” category:
If we check the two misclassified reviews of this last evaluation, we see that there are no references unambiguous enough to let us guess the type of restaurant they are. In these cases, the fact that the model is not able to determine the type of restaurant (nor us, for that matter) is quite reasonable.
As you can see, defining your own model is quite easy, specially with the help of our Excel add-in to make the training and optimizing process more agile.
If you have any questions, we’ll be happy to answer them at email@example.com.