Getting Started with Orange 17: Text Clustering Video Lecture | Orange Data for Beginners - Data & Analytics

In the previous video on text mining,
we talked about text preprocessing.
Now our data should be ready for machine learning, right?
Well, not quite.
After preprocessing,
Orange still sees only lines in lines of text.
For machine learning we need to transform text
into numerical representation
and a simple way to do it is to count
how many times each word appears in the text.
This approach is called bag of words.
Let us reuse the workflow from our previous lesson.
Corpus reads the collection of text documents
and text preprocessing removes stop words and delimiters.
Now we will extend the workflow with a Bag of Words widget.
Bag of Words outputs the data table
where word counts are the new added features.
You can always check the output of bag of words in a data table.
Great! Now we have our data matrix
and we can find interesting groups of documents.
Connect Distances to Bag of Words.
Here, we will use cosine distance
as it normally works best for corpora.
We feed computed distances to Hierarchical Clustering.
To estimate the distances between clusters
we will select Ward linkage.
Now drag a line at the top of the visualization left
and right.
What is the appropriate number of groups?
Two seems to make the most sense.
The nodes in our dendrogram also have a label.
In folkloristics, Grimm's tales are labeled with Aarne-Thompson-Uther index
which defines the topic of the tale.
If the tale talks about animals, it's an animal tale.
If it's more about dragons and princesses, it's a tale of magic.
Looks like the tale type corresponds quite well with our clusters.
Except for one part where animal tales and tales of magic are mixed.
Can we figure out why they're mixed?
Select the cluster
and connect Corpus Viewer to Hierarchical Clustering.
Seem like some tales of magic still mention animals.
Perhaps clustering got it right after all.
Clustering is a great way to uncover similar documents in unlabeled text.
But here, we actually have labels -
the ATU Topic.
In the next video we will talk about classification
and try to predict the type of the tale on fresh data.