Up coming, it calculates the probabilities of receiving the phrase ‘good’ in both of those a “Optimistic” As well as in a “Negative” review. It does exactly the same for your word “product”.

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Guaranteeing that the document has a clear and rational structure with correct headings, subheadings, and also a desk of contents.

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Because Naive Bayes will work best when many of the unbiased variables are categorical, we shall convert the frequency of each phrase into an element variable with labels “Indeed” if frequency is in excess of 0, and “No” if it is equivalent to 0. Then we break up the data into train and take a look at datasets from the ratio of eight:two. Making use of basic prop.table()