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That's an interesting problem, thanks for bring out here on stack.

I think this problem is similar to when we apply LSA(Latent Semantic Analysis) in sentiment analysis to find list of positive and negative words with polarity with respect to some predefined positive and negative words.

Good reads:


Learning Word Vectors for Sentiment Analysis
Unsupervised Learning of Semantic Orientation from a Hundred-Billion-Word Corpus


So, according to me LSA is your best approach to begin with in this situation as it learns the underlying relation between the words from the corpus and probably that's what you are looking for.