Building Machine Learning Systems with Python - Third Edition

Using all classes

Once again, we simplified our task a bit, since we used only positive or negative tweets. That means, we assumed a perfect classifier that classified upfront whether the tweet contains a sentiment and forwarded that to our Naïve Bayes classifier.

So, how well do we perform if we also classify whether a tweet contains any sentiments at all? To find that out, let's first write a convenience function that returns a modified class array that provides a list of sentiments that we would like to interpret as positive:

def tweak_labels(Y, pos_sent_list):    pos = Y==pos_sent_list[0]    for sent_label in pos_sent_list[1:]:        pos |= Y==sent_label
        Y = np.zeros(Y.shape[0])    Y[pos] = 1    return Y.astype(int)  

Note that we are talking about two ...