Machine Learning for Everyone

This is that Latent semantic analysis (LSA) do. It is based on how frequent you see the word on the exact topic. Like, there are more tech terms in tech articles, for sure. The names of politicians are mostly found in political news, etc. Yes, we can just make clusters from all the words at the articles, but we will lose all the important connections (for example the same meaning of battery and accumulator in different documents). LSA will handle it properly, that's why its called "latent semantic". So we need to connect the words and documents into one feature to keep these latent connections.