This is just my personal interpretation, but the way I approach is I create a trustworthiness score for the set of reviews. For example, too many 5 stars without comments and without low stars might earn a low trustworthiness score because the distribution looks unnatural compared to the distribution in similar competitors. Or a set of 6 reviews, no matter their distribution will also receive a low trustworthiness score simply because there's not much to go on. Reviews of a restaurant complaining about a particularly egregious experience earn a low trustworthiness score since it looks like an outlier and there's emotions running wild. Review sets with a decent number of 3 star reviews containing several well-articulated paragraphs tend to earn a high trustworthiness score. Etc.

So, rather than the score being a dimensionless good-or-bad scale, it's a meta analysis of the reviews, judged on multiple dimensions. This means that some companies/restaurants/products simply don't provide enough information for me to form a conclusion despite having a number of reviews that lean towards either "good" or "bad". And that's ok, because the very fact that I've considered so many different dimensions also tells me that there isn't necessarily a single absolute best option.

This, in my mind, seems like a more accurate depiction of reality than blindly ranking by number of 5 star reviews.