Features Extraction from News Blog
Ok so what you need to do is set up a pandas dataframe.
In the first column, you need to have the entire text of the website or blog, processes as you mentioned.  In the following columns, you need to have one column for each tag you want to apply to the dataset, one-hot encoded.
Then you will need to fill out the rows by labeling, by hand, several thousand of the website or blog posts using that one-hot encoding.
When you are done,  you can train your machine.  Then any new article you put into it, the machine will output a probability for the tags belonging to that article.  There are probably a lot of repositories on github with pretrained models you can use.
I am new to data science/Machine Learning.
I have to write a web crawler and extract features out of each blog. These features in the form of Tags tell about the industry, specific products, tools, and similar things like these.
I have done part of scraping but now I am stuck with entities identification. 
I did Data processing (Tokenization, data cleaning, removing stop words/punctuation, stemming/lemmatization).
for now, what I have to do for feature extractions?