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My dataset consists of apparel images and their corresponding product descriptions as below:

Meet your simplest summer outfit. Designed with a relaxed straight-leg fit, the Super-Soft Summer Jean Coverall features a buttoned front, side pockets, and a short-sleeve silhouette that’s great for warm weather.

Let us say that we focus on one particular aspect of apparels, say 'Season'. Now, say using text classification or using image classification, I am able to classify the dress to belong to the 'Summer' class. The goal is to be able to find words/phrases which are related to 'Summer', for example, "great for warm weather". So, considering the entire dataset, for every class within the 'Season' attribute, we would have a list of associated phrases/words. Something like this:

{
"Summer" : ["warm weather", "warm weather days", "keeps you cool"],
"Winter" : ["cozy winter knit", "knit dress"]
}

I was thinking that BERT's masked language modeling could come to the rescue, but I am not exactly clear about it. Any suggestions please?

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