NLTK Sklearn Genism Text to Topic I aint no data scientist/machine learner.

What Im Lookin for

text = "Donald Trump became the president of America"
#some data science works
print(topics) 
#prints ["politics"]

text = "Rihanna is starring in the new movie The Inception 2"
#some data science works
print(topics) 
#prints ["movie","music"]


What I can do

I can extract words like Donald Trump,America,Rihanna using POS

I can get huge paragraphs/lists of articles/words on politics,movies etc and save them in a text file.

What i can't do

Make meaningful topics out of  those words like sports,politics,movies

What i want you do to

Point me in the right specific direction towards solving this problem aka enlighten me
I am staying quite generic since you asked for enlightenment, just mentioning some possible directions that you can explore.

You have basically two possibilities:


Classification of the text (Supervised learning). 
Supervised means
that you need first to externally apply labels (for example manually by humans) to examples of texts
(labels could be "politics" or "show") and then use one of the
classification algorithms.  You have extracted words from the text,
so you could use a "bag of words" approach for classifying.  There exist adaptations
of classification algorithms (multi-label classification) in order to
provide multiple labels (such as one text is labelled both with
"music" and "movie"). 
You can find text corpora already pre-labelled, to train the algorithm and partly avoid the manual effort.    
Clustering the text, topic modelling (unsupervised learning).
In this case you do not need to provide examples with labels but the algorithm will cluster the texts according to parameters like the similarity of two texts or on keywords/topics extracted from the texts. 
Although this method do not require labeled examples, the clusters you get as output will require fine tuning, e.g. number of clusters to produce, names of the clusters, etc.


Since you mentioned SKlearn, you can find some directions on their web site: Text Feature Extraction. 

Here is a comprehensive (bit older) summary: Machine Learning in Automated Text Categorization by Fabrizio Sebastiani