Building Machine Learning Systems with Python - Richert, Coelho

Classification II – Sentiment Analysis
Fetching the Twitter data
Naturally, we need tweets and their corresponding labels that tell us whether a
tweet contains positive, negative, or neutral sentiment. In this chapter, we will use
the corpus from Niek Sanders, who has done an awesome job of manually labeling
more than 5000 tweets and granted us permission to use it in this chapter.
To comply with Twitter's terms of services, we will not provide any data from
Twitter nor show any real tweets in this chapter. Instead, we can use Sanders'
hand-labeled data, which contains the tweet IDs and their hand-labeled sentiment,
and use his script, install.py, to fetch the corresponding Twitter data. As the
script is playing nicely with Twitter's servers, it will take quite some time to
download all the data for more than 5000 tweets. So it is a good idea to start
it now.
The data comes with four sentiment labels:
>>> X, Y = load_sanders_data()
>>> classes = np.unique(Y)
>>> for c in classes:
print("#%s: %i" % (c, sum(Y==c)))
#irrelevant: 543
#negative: 535
#neutral: 2082
#positive: 482
We will treat irrelevant and neutral labels together and ignore all non-English
tweets, resulting into 3642 tweets. These can be easily filtered using the data
provided by Twitter.
Introducing the Naive Bayes classifier
Naive Bayes is probably one of the most elegant machine learning algorithms
out there that is of practical use. Despite its name, it is not that naive when you
look at its classification performance. It proves to be quite robust to irrelevant
features, which it kindly ignores. It learns fast and predicts equally so. It does
not require lots of storage. So, why is it then called naive?
The naive was added to the account for one assumption that is required for Bayes
to work optimally: all features must be independent of each other. This, however,
is rarely the case for real-world applications. Nevertheless, it still returns very good
accuracy in practice even when the independent assumption does not hold.
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