Word Frequency Analysis of Document Sets
There are many algorithm to do classification: Naïve Bayes, logistic regression, SVM, decision tree..etc. My suggestion is to try Naïve Bayes first by calculating below probabilities which a new document belongs to $class_{good}$ or $class_{bad}$. (https://web.stanford.edu/class/cs124/lec/naivebayes.pdf)

$$
P(Class_{good} \vert document_{new}) = \frac{P(document_{new} \vert Class_{good}) \cdot P(Class_{good}) }{P(document_{new})}
$$

$$
P(Class_{bad} \vert document_{new}) = \frac{P(document_{new} \vert Class_{bad}) \cdot P(Class_{bad}) }{P(document_{new})}
$$

And generally, when we are doing text mining questions, we will do several preprocess on one document: 


Tokenization(1-gram/bigram/...etc)
remove stop words ('a', 'the' 'at', ... etc)
Stemming: transforms a word into its root form. (studied => study)


My suggestion is to do above preprocesses and try more features not just the words in one document, if there are some metadataes.