I am training a big corpus using word2vec and averaging the word vectors to get sentence vectors. What is the best way to address negative words so that negative and positive sentences are away from each other? For e.g.: After the fix code worked and After the fix code did not work should ideally give sentence vectors which are far from each other. I heard one approach is to look for negative words like not and negate the next word vector. Can someone please clarify if that's a good approach or can suggest a better approach? 
How to handle negative words in word2vec?
machine learning;neural network;nlp;word2vec