You basically have a text classification or document classification problem. This is a specific case of binary classification, which is itself a specific case of supervised learning. It's well studied problem, there are many tools to do it. Basically you give a set of good documents and bad documents to a learning or training process, which finds words that correlate strongly with positive and negative documents and it outputs a function capable of classifying unseen documents as positive or not. Naive Bayes is the simplest learning algorithm for this kind of task, and it will do a decent job. There are fancier algorithms like Logistic Regression and Support Vector Machines which will probably do a somewhat better, but they are more complicated.
To determine which variants words are actually equivalent to each other, you want to do some kind of stemming. The Porter stemmer is a common choice here.