Not sure how to phrase this question properly, but this is what I intend to achieve using the hypothetical scenario outlined below - 
A user's email to me has just the SUBJECT and BODY, the subject being the topic of email, and the body being a description of the topic in just one paragraph of max 1000 words. Now I would like to analyse this paragraph (in the BODY) using some computer language (python, maybe), and then come up with a list of most important words from the paragraph with respect to the topic mentioned in the SUBJECT field.
For example, if the topic of email is say iPhone, and the body is something like "the iPhone redefines user-interface design with super resolution and graphics. it is fully touch enabled and allows users to swipe the screen"
So the result I am looking for is a sort of list with the key terms from the paragraph as related to iPhone. Example - (user-interface, design, resolution, graphics, touch, swipe, screen). 
So basically I am looking at picking the most relevant words from the paragraph. I am not sure on what I can use or how to use to achieve this result. Searching on google, I read a little about Natural Language Processing and python and classification etc. I just need a general approach on how to go about this - using what technology/language, which area I have to read on etc..
Thanks!

EDIT:::

I have been reading up in the meantime. To be precise, I am looking at HOW TO do this, using WHAT TOOL:
Generate related tags from a body of text using NLP which are based on synonyms, morphological similarity, spelling errors and contextual analysis.
I am not an expert but it seems like you really need to define a notion of "key term", "relevance", etc, and then put a ranking algorithm on top of that. This sounds like doing NLP, and as far as I know there is a python package called NLTK that might be useful in this field. Hope it helps!
picking the most relevant words from a paragraph