How to use Non Negative Matric Factorization (NMF)'s transform method to project topics on new text data
This is how you should get your topics and corresponding words. You first get the NMF transformation of your new input and sort its components. The biggest components tell you which features to look at in nmf.components_, which links topics to words (your tfidf features). Finally, you print the k most important words of the topics. Topics and words scores are just calculated by dividing their value by the sum of all values.  

    import numpy as np
    # Transform new_feedback to NMF space
    nmf_new_feedback = clf.transform(vectorizer.fit(new_feedback))
    # top 10 topics of the new feedback
    compo = np.argsort(nmf_new_feedback)[-10:][::-1]
    # Connection between indices and words of tfidf
    feature_names = vectorizer.get_feature_names()
    for topic_idx in compo:
        # Current nmf topic
        topic = nmf.components_[topic_idx]
        print("Topic number: ", topic_idx)
        print("Topic score: ", nmf_new_feedback[topic_idx]/sum(nmf_new_feedback))
        # Top 15 words of current topic
        for k in topic.argsort()[-15:][::-1]]
            print("Word: ",feature_names[k], "  Score: ", topic[k]/ sum(topic))