Is it possible to create a stopword filter, when using tokenizer = " keyword"?

Right now, I'm able to create a working stopword filter in the following way.

company_name_stopword = ["inc","corp"]
_company_name_stopword_filter = dsl.token_filter("_company_name_stopword_filter",
    ignore_case = "true",

_tag_analyzer = dsl.analyzer('tag_analyzer',

response = _tag_analyzer.simulate(
    text = 'Apple Inc',
    using = localhost)
print([t.token for t in response.tokens])

The objective is to remove from text input and string in the list company_name_stopword.

However, the index I'm using, uses tokenizer="keyword" instead of tokenizer="whitespace".

Is it possible to create this filter with a keyword tokenizer? I can't change the tokenizer of the index without incurring into potential large behavioural/performance changes...

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