Q: How to perform text mining on an online news article? I am new to data mining and currently working on an online news article from TOI. My aim is to get some useful information out of this text which is not clear when you read the article and the most important things mentioned in it.
I am done with document processing on the article "TOI manifesto" which I have made it in a simple text format. I have applied tokenization, filtering and ngrams and got the result as shown in the images. 


I don't know how to proceed further, which algorithm / model would be best to apply for mining this article and what should be my next step? 

A: It sounds like you need to do some work inspecting which features were useful to the classifier, for making a judgement. Depending on the classification algorithm you're using, there are various approaches to this. One approach, would be to calculate the mutual information for each feature:
$I(X;Y)=\sum_{y}\sum_{x}p(x,y)\log(\frac{p(x,y)}{p(x)p(y)})$
Other approaches also exist. A fantastic resource for learning about these I can recommend s George Forman's "Feature Selection for Text Classification" (start at page 12, for your task). You can use many of these feature selection methods to quantify the contribution of your input features.
