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Naive bayesian classifier problem

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1. You have algorithmic trading machine, which automatically buys TSLA when a twitter message by Elon Musk only is Positive and short sell TSLA when it is Negative, using Naïve Bayesian. He just tweeted! Tweet: “ Blow the whistle” Naïve Bayesian classifier related info: You randomly selected 10,000 messages from Twitter and manually classified into Positive, Neutral, and Negative categories. It turns out that 3,000 of them are Positive, 4,000 are Neutral, and 3,000 are Negative. In each group, the number of messages that have the word “blow” are 60 (Positive), 100 (Neutral), 300 (Negative), respectively. Also, in each group, the number of messages that have the word “whistle” are 120 (Positive), 200 (Neutral), and 400 (Negative), respectively. Simply based on these two words, determine what the machine would do.

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