in this casewe have six total tweetsout of which four are positive and two negativewhich results in the following priorsthis meanswithout knowing anything about the tweet itselfwe would be wise in assuming the tweet to be positive and the piece that is still missing is the calculation of probabilities for the two features and conditioned on class which are the 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' say we want to know the probability of seeing awesome occurring once in tweet knowing that its class is "positive"we would have the followingsince out of the four positive tweets three contained the word awesomeobviously the probability for not having awesome in positive tweet is its inverse as we have seen only tweets with the counts or similarly for the rest (omitting the case that word is not occurring in tweet)for the sake of completenesswe 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