How to find likelihood in NLTK -


http://www.laurentluce.com/posts/twitter-sentiment-analysis-using-python-and-nltk/comment-page-1/#comment-73511

i trying understand nltk using link. cannot understand how values of feature_probdist , show_most_informative_features computed.

esp when word "best" not come how likelihood computed 0.077 . trying since long

that because explaining code nltk's source code not displaying of it. full code available on nltk's website (and linked in article referenced). these field within method , method (respectively) of the naivebayesclassifier class within nltk. class of course using naive bayes classifier, modification of bayes theorum strong (naive) assumption each event independent.

  • feature_probdist = "p(fname=fval|label), probability distribution feature values, given labels. expressed dictionary keys (label,fname) pairs , values probdistis on feature values. i.e., p(fname=fval|label) = feature_probdist[label,fname].prob(fval). if given (label,fname) not key in feature_probdist, assumed corresponding p(fname=fval|label) 0 values of fval."

  • most_informative features returns "a list of 'most informative' features used classifier. purpose of function, informativeness of feature (fname,fval) equal highest value of p(fname=fval|label), label, divided lowest value of p(fname=fval|label), label:"

    max[ p(fname=fval|label1) / p(fname=fval|label2) ]

check out the source code entire class if still unclear, article's intent not break down how nltk works under hood in depth, rather give basic concept of how use it.


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