Building Machine Learning Systems with Python - Richert, Coelho

Classification II – Sentiment
Analysis
For companies, it is vital to closely monitor the public reception of key events such
as product launches or press releases. With real-time access and easy accessibility of
user-generated content on Twitter, it is now possible to do sentiment classification
of tweets. Sometimes also called opinion mining, it is an active field of research in
which several companies are already selling their products. As this shows that a
market obviously exists, we have motivation to use our classification muscles built
in the previous chapter to build our own home-grown sentiment classifier.
Sketching our roadmap
Sentiment analysis of tweets is particularly hard because of Twitter's size limitation
of 140 characters. This leads to a special syntax, creative abbreviations, and seldom
well-formed sentences. The typical approach of analyzing sentences, aggregating
their sentiment information per paragraph and then calculating the overall sentiment
of a document, therefore, does not work here.
Clearly, we will not try to build a state-of-the-art sentiment classifier. Instead, we
want to:
• Use this scenario as a vehicle to introduce yet another classification
algorithm: Naive Bayes
• Explain how Part Of Speech (POS) tagging works and how it can help us
• Show some more tricks from the scikit-learn toolbox that come in handy
from time to time