Building Machine Learning Systems with Python - Richert, Coelho

Classification II – Sentiment Analysis
In this case, we have six total tweets, out of which four are positive and two negative,
which results in the following priors:
This means, without knowing anything about the tweet itself, we would be wise in
assuming the tweet to be positive.
The piece that is still missing is the calculation of and , which are the
probabilities for the two features and conditioned on class C.
This is calculated as the number of tweets in which we have seen that the concrete
feature is divided by the number of tweets that have been labeled with the class of
. Let's say we want to know the probability of seeing awesome occurring once in a
tweet knowing that its class is "positive"; we would have the following:
Since out of the four positive tweets three contained the word awesome, obviously
the probability for not having awesome in a positive tweet is its inverse as we have
seen only tweets with the counts 0 or 1:
Similarly for the rest (omitting the case that a word is not occurring in a tweet):
For the sake of completeness, we will also compute the evidence so that we can see
real probabilities in the following example tweets. For two concrete values of and
,we can calculate the evidence as follows:
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