BAYESIAN SENTIMENT ANALYSIS WITH RUBY ΛRUBY 

BAYES’ THEOREM 

Posterior: the probability of our hypothesis being true given the data collected BAYES’ THEOREM 

Posterior: the probability of our hypothesis being true given the data collected Likelihood: The probability of collecting this data when our hypothesis is true BAYES’ THEOREM 

Posterior: the probability of our hypothesis being true given the data collected Likelihood: The probability of collecting this data when our hypothesis is true Prior: The probability of the hypothesis being true before collecting any data BAYES’ THEOREM 

Posterior: the probability of our hypothesis being true given the data collected Priori: The probability of collecting this data under all possible hypotheses Likelihood: The probability of collecting this data when our hypothesis is true Prior: The probability of the hypothesis being true before collecting any data BAYES’ THEOREM 

BAYES’ THEOREM ON TWEETS P(positive | tweet) = P(tweet | positive) P(positive) P(tweet) 

THE MATH P(positive | tweet) = P(tweet | positive) P(positive) P(tweet) 

THE MATH P(positive | tweet) = P(tweet | positive) P(positive) P(tweet) tweet = “I really liked this movie” P(tweet) = This is a constant so we’ll disregard it for now. P(x) = Probability of x occurring P(positive) = 0.5 Since we only have 2 classes (negative and positive) there is a 50% chance that something is positive. P(tweet | positive) = number of times the words in the tweet were in a tweet marked as positive in the training set divided by the total number of words marked as positive in the training set. HOW DO WE GET THIS? 

THE MATH P(tweet | positive) = number of times the words in the tweet were in a tweet marked as positive in the training set divided by the total number of words marked as positive in the training set. tweet = “I really liked this movie” Number of times ‘liked’ occurs in positive tweets = 1623 Number of words in all positive tweets = 2342 1623 / 2342 = 0.692 probability that a tweet that says ‘liked’ will be a positive tweet 69.2% 

THE MATH P(positive | tweet) = P(tweet | positive) P(positive) P(tweet) tweet = “I really liked this movie” P(tweet) = This is a constant so we’ll disregard it for now. P(x) = Probability of x occurring P(positive) = 0.5 Since we only have 2 classes (negative and positive) there is a 50% chance that something is positive. P(tweet | positive) = 0.692 The Number of times the words in the tweet were in a tweet marked as positive in the the training set divided by the total number of words marked as positive in the training set. P(positive | tweet) = 0.692 x 0.5 

THE MATH P(positive | tweet) = P(tweet | positive) P(positive) P(tweet) tweet = “I really liked this movie” P(tweet) = This is a constant so we’ll disregard it for now. P(x) = Probability of x occurring P(positive) = 0.5 Since we only have 2 classes (negative and positive) there is a 50% chance that something is positive. P(tweet | positive) = 0.692 The Number of times the words in the tweet were in a tweet marked as positive in the the training set divided by the total number of words marked as positive in the training set. 0.346 = 0.692 x 0.5 34.6% Probability that the tweet is positive 

WHAT ABOUT RUBY? 

BAYES’ IN RUBY 

BAYES’ IN RUBY 

BAYES’ IN RUBY 

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