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.htmlHowever, 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, categorychiense restarant near me, chinesefoodloverjapanese food near me, japanesefoodloverI can break it down to [chiense, restarant, near, me], chinesefoodlover[japanese, food, near, me], japanesefoodloverBut then how to draw clusters of classes from there.
How to do scatterplot Visualization of Text Classification
classification;text mining;visualization;multiclass classification