Building Machine Learning Systems with Python - Richert, Coelho

Classification II – Sentiment Analysis
Solving an easy problem first
As we have seen when we looked at our tweet data, the tweets are not just positive
or negative. The majority of tweets actually do not contain any sentiment, but
are neutral or irrelevant, containing, for instance, raw information (New book:
Building Machine Learning ... http://link). This leads to four classes. To
avoid complicating the task too much, let us for now only focus on the positive and
negative tweets:
>>> pos_neg_idx=np.logical_or(Y=="positive", Y=="negative")
>>> X = X[pos_neg_idx]
>>> Y = Y[pos_neg_idx]
>>> Y = Y=="positive"
Now, we have in X the raw tweet texts and in Y the binary classification; we assign 0
for negative and 1 for positive tweets.
As we have learned in the chapters before, we can construct TfidfVectorizer
to convert the raw tweet text into the TF-IDF feature values, which we then use
together with the labels to train our first classifier. For convenience, we will use the
Pipeline class, which allows us to join the vectorizer and the classifier together and
provides the same interface:
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.naive_bayes import MultinomialNB
from sklearn.pipeline import Pipeline
def create_ngram_model():
tfidf_ngrams = TfidfVectorizer(ngram_range=(1, 3),
analyzer="word", binary=False)
clf = MultinomialNB()
pipeline = Pipeline([('vect', tfidf_ngrams), ('clf', clf)])
return pipeline
The Pipeline instance returned by create_ngram_model() can now be used for
fit() and predict() as if we had a normal classifier.
Since we do not have that much data, we should do cross-validation. This time,
however, we will not use KFold, which partitions the data in consecutive folds, but
instead we use ShuffleSplit. This shuffles the data for us, but does not prevent the
same data instance to be in multiple folds. For each fold, then, we keep track of the
area under the Precision-Recall curve and the accuracy.
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