Q&A answer comparison multiple sentences using I have been working on a Q&amp;A app that has a template of questions and answers. The hope is to take answer text from the user, and compare it to the correct answer. I’d like to weight it on the keywords/buzzwords and general accuracy of the response. 

I’m a mobile developer, not quite a data scientist or well versed in machine learning, so I’m a bit lost in the woods here and not sure if I’m going down the right path.

I’ve been looking into natural language process and some related python libraries/models that evaluate sentences.    

Am I headed in the right direction? or should I go about this another way?
It depends on how sophisticated you want the system to be:


the most basic way is to compare the user answer with the gold answer using a simple string similarity measure, such as the overlap coefficient. Basically it just counts the words in common, and there would be a minimum threshold to count the answer as correct (e.g. 80% words in common). It's not very good because a small typo is enough to make the score wrong and it gives the same importance to every word.
The same idea but with TF-IDF weights, typically with cosine similarity. This requires a corpus on which to calculate the IDF weights (which reflect the importance of the words in general).
Still based on string similarity measures but more advanced: a hybrid similarity measure which combines character-level similarity between words (e.g. Jaro, Levenshtein edit distance) and similarity across words. Soft-TFIDF is a common example. Disadvantage: can be tricky to properly adapt to the task.
Beyond that there are a lot of fancy options: using semantic similarity with WordNet (synonyms), words embeddings, etc.


Note: fyi this is not related to the task called Question Answering, which is about a computer-generated answer to a question.