Question: A classifier is trained with documents having the following counts for key sentiment words, with positive or negative class assigned as noted. good poor |

 A classifier is trained with documents having the following counts for

A classifier is trained with documents having the following counts for key sentiment words, with positive or negative class assigned as noted. "good" "poor" | "great" (class) d1. 3 0 3 pos d2 0 1 2 d3. 3 0 neg 5 2 neg 0 2 0 neg pos 1 1 d4. d5. Give the polarity of the following review when using standard Nave Bayes and when using a Binarized Nave Bayes (Use Add-1 smoothing with both methods.) There's just something enjoyable about a movie that's hopelessly committed to its (very bad) vision. Whether it's due to bad special effects, awful acting, or a completely absurd or nonsensical plot, these films create a sense of sheer wonderment and force you to exclaim, "How is this a movie?!" But the mere fact that something so illogical, or low-budget, or ill-conceived exists is at the root of why we like these movies. They're so bad that ... they're actually kind of good. Because it's summertime the season when so many Good Bad Movies have bloomed we wanted to give the subgenre the attention it deserves. We'll be exploring the genre at length, but no project would be complete without a big list that definitively determines the greatest Good Bad Movies to ever be released. It was a Herculean task (and in this case we're specifically using that adjective with the Rock's Hercules in mind); here's how we did it

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