In this example a list of sample reviews and their corresponding classes are zipped into an association list to be passed into the makeMaterial function. This newly created material is then passed into the runBayes function, along with a new review. This will classify the new review based on the training material that has been given.

data Class = Positive | Negative deriving (Eq, Show)

doc1 = "I loved the movie"
doc2 = "I hated the movie"
doc3 = "a great movie. good movie"
doc4 = "poor acting"
doc5 = "great acting. a good movie"

docs = [doc1, doc2, doc3, doc4, doc5]
correspondingClasses = [Positive, Negative, Positive, Negative, Positive]
classifiedDocs = zip docs correspondingClasses

main :: IO ()
main = do
    -- teachMultiple returns a BayesModel Class
    let teachMultiple = foldl (\m (sample, cl) -> teach (T.pack sample) cl m) emptyModel

    let review = "I hated the poor acting"
    let result = runBayes (teachMultiple classifiedDocs) review
    
    putStrLn $ "The review '" ++ review ++ "' is " ++ show result -- Negative