I am working on my first ever ds problem on classification. I have got end to end process but not much visualization except bar charts and pie charts.
I want to see how well classes are distinguished in sample dataset itself to find anomaly in sample data or to see how many outliers sample have.
I see many nice scatterplot clustering done in many articles, pappers and books which are mostly based on numerical features. Something like following taken from scikit learn : http://scikit-learn.org/stable/auto_examples/datasets/plot_random_dataset.html
However, most of those are for numeric features. How can I draw similar plot for textual features?
One way I can do it is converting all features into vectors; plot all the indexes(token indexes) on x-axis and their respective metric (TF or TF-IDF) on y-axis? But then there are so many features!?
Also, how can I analyze raw sample data in such clustering format without transforming them into vectors. I can do tokenization and some basic normalization.
For example my sample data is text, category
"chiense restarant near me", chinesefoodlover "japanese food near me", japanesefoodlover
I can break it down to
[chiense, restarant, near, me], chinesefoodlover [japanese, food, near, me], japanesefoodlover
But then how to draw clusters of classes from there.