How to implement keyword based text clustering?

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I have 4 topics and 10 keywords representing each of those 4 topics. I now want to classify all the documents in my dataset in one of these 4 topics using the keywords extracted for each topic.

topic0 = ["gene","rna","expression","mouse","assay","activity","concentration","target","ace","lung"]

topic1 = ["age","pneumonia","hospital","risk","outcome","incidence","diagnosis","strain","lung","child"]

topic2 = ["intervention","wuhan","city","contact","people","scenario","peak","confirmed_case","quarantine","daily"]

topic3 = ["sequence","genome","host","structure","gene","specie","rna","read","strain","mutation"]

These are the keywords for each topic and I have 1200 documents in my datatset. How do I classify them now?

Maybe some sort of similarity algorithm can be used for this. Please help!! Im confused

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