# I keep this as a cell in my title slide so I can rerun 
# it easily if I make changes, but it's low enough it won't
# be visible in presentation mode.
%run talktools

!sudo pip install -U textblob
!sudo pip install -U nltk textblob-aptagger
!sudo python -m textblob.download_corpora

import nltk
nltk.download('wordnet')

import textblob

blob = textblob.TextBlob(u'   Welcome to textblob: text analytics for humans   ')

print type(blob)
print blob[5:10]
print blob.title()
print blob.find('text')
print blob.endswith(' ')
print blob.stripped

print textblob.Word
print textblob.Word(u'sweet').pluralize()
print textblob.Word(u'sweets').singularize()
print textblob.Word(u'running').lemmatize('v')

print type(blob.words)
print blob.words.singularize()
print blob.words.count('text')

blob = textblob.TextBlob(u'why is it so hot in here?!\nthis is not hot at all')

print blob.words
print type(blob.words), type(blob.words[0])
print blob.sentences
print blob.ngrams()
print blob.word_counts['hot']

print blob.tokenizer

import nltk.tokenize

blob.tokenizer = nltk.tokenize.LineTokenizer()
print blob.tokenize()
print blob.tokenize(nltk.tokenize.PunktWordTokenizer())

blob = textblob.TextBlob(u'Ray Charles has got Georgia on his mind')

print blob.noun_phrases
print blob.pos_tags
print blob.parse()

import textblob.np_extractors

print blob.np_extractor
blob = textblob.TextBlob(u'Ray Charles has got Georgia on his mind',
                         np_extractor=textblob.np_extractors.ConllExtractor())
print blob.noun_phrases

import textblob.taggers

print blob.pos_tagger
blob = textblob.TextBlob(u'Ray Charles has got Georgia on his mind',
                         pos_tagger=textblob.taggers.NLTKTagger())
print blob.pos_tags

blob = textblob.TextBlob(u'I am runing across the boarder')

print blob.correct() # based on Pattern
print blob.words[2].spellcheck()

blob = textblob.TextBlob(u'the weather is fantastic!')

print blob.sentiment

import textblob.sentiments

print blob.analyzer
blob.analyzer = textblob.sentiments.NaiveBayesAnalyzer()
print blob.sentiment

training = [
            (u'tobey maguire is a terrible spiderman.','pos'),
            (u'a terrible Javert (Russell Crowe) ruined Les Miserables for me...','pos'),
            (u'The Dark Knight is the greatest superhero movie ever!','neg'),
            (u'Fantastic Four should have never been made.','pos'),
            (u'Wes Anderson is my favorite director!','neg'),
            (u'Captain America 2 is pretty awesome.','neg'),
            (u'Let\s pretend "Batman and Robin" never happened..','pos'),
            ]
testing = [
           (u'Superman was never an interesting character.','pos'),
           (u'Fantastic Mr Fox is an awesome film!','neg'),
           (u'Dragonball Evolution is simply terrible!!','pos')
           ]

import textblob.classifiers

classifier = textblob.classifiers.NaiveBayesClassifier(training)
print classifier.accuracy(testing)
classifier.show_informative_features(3)

blob = textblob.TextBlob(u'the weather is terrible!', classifier=classifier)
print blob.classify()

np_extractor = textblob.np_extractors.ConllExtractor()
pos_tagger = textblob.taggers.NLTKTagger()
tokenizer = nltk.tokenize.PunktWordTokenizer()
analyzer = textblob.sentiments.NaiveBayesAnalyzer()

blob = textblob.TextBlob(u'Dog goes woof. Cat goes meow. Bird goes tweet. And mouse goes squeek.',
                         np_extractor=np_extractor,
                         pos_tagger=pos_tagger,
                         tokenizer=tokenizer,
                         analyzer=analyzer)
# do something with blob

blob2 = textblob.TextBlob(u'Cow goes moo. Frog goes croak. And the elephant goes toot.',
                          np_extractor=np_extractor,
                          pos_tagger=pos_tagger,
                          tokenizer=tokenizer,
                          analyzer=analyzer)
# do something with blob2

blob3 = textblob.TextBlob(u'Ducks say quack. And fish go blub. And the seal goes ow ow ow ow ow.',
                          np_extractor=np_extractor,
                          pos_tagger=pos_tagger,
                          tokenizer=tokenizer,
                          analyzer=analyzer)
# do something with blob3

blobber = textblob.Blobber(
                           np_extractor=np_extractor,
                           pos_tagger=pos_tagger,
                           tokenizer=tokenizer,
                           analyzer=analyzer)

blob = blobber(u'But there\'s one sound that no one knows: What does the fox say?')

print blob
print blob.np_extractor
print blob.pos_tagger
print blob.tokenizer
print blob.analyzer

print map(blobber, ['Ring-ding-ding-ding-dingeringeding!',
                    'Wa-pa-pa-pa-pa-pa-pow!',
                    'Hatee-hatee-hatee-ho!',
                    'Joff-tchoff-tchoffo-tchoffo-tchoff!'])

training = [
            (u'tobey maguire is a terrible spiderman.','pos'),
            (u'a terrible Javert (Russell Crowe) ruined Les Miserables for me...','pos'),
            (u'The Dark Knight is the greatest superhero movie ever!','neg'),
            (u'Fantastic Four should have never been made.','pos'),
            (u'Wes Anderson is my favorite director!','neg'),
            (u'Captain America 2 is pretty awesome.','neg'),
            (u'Let\s pretend "Batman and Robin" never happened..','pos'),
            ]
texts = [
         u'Superman was never an interesting character.',
         u'Fantastic Mr Fox is an awesome film!',
         u'Dragonball Evolution is simply terrible!!'
         ]

import textblob.classifiers

nb_classifier = textblob.classifiers.NaiveBayesClassifier(training)
dt_classifier = textblob.classifiers.DecisionTreeClassifier(training)

for text in texts:
    nb_class = textblob.TextBlob(text, classifier=nb_classifier).classify()
    dt_class = textblob.TextBlob(text, classifier=dt_classifier).classify()
    print nb_class == dt_class

nb_blobber = textblob.Blobber(classifier=nb_classifier)
dt_blobber = textblob.Blobber(classifier=dt_classifier)

for text in texts:
    print nb_blobber(text).classify() == dt_blobber(text).classify()