hop of those help? I have various restaurant labels with me and i have some words that are unrelated to restaurants as well. like below: , Word2Vec could help with this, but key factors to consider are:
Does that help No, not really. For reference, common word2vec models which are trained on wikipedia (in english) consists around 3 billion words. You can use KNN (or something similar). Gensim has the most_similar function to get the closest words. Using a dimensional reduction (like PCA or tsne) you can get yourself a nice cluster. (Not sure if gensim has tsne module, but sklearn has, so you can use it) btw you're referring to some image, but it's not available.
Use Word2vec to determine which two words in a group of words is most similar
hope this fix your issue @rylan-feldspar's answer is generally the correct approach and will work, but you could do this a bit more compactly using standard Python libraries/idioms, especially itertools, a list-comprehension, and sorting functions. For example, first use combinations() from itertools to generate all pairs of your candidate words: