How to use textblob - 10 common examples

('I am tired of this stuff.', 'neg'),
    ("I can't deal with this", 'neg'),
    ('He is my sworn enemy!', 'neg'),
    ('My boss is horrible.', 'neg')
]

test = [
    ('The beer was good.', 'pos'),
    ('I do not enjoy my job', 'neg'),
    ("I ain't feeling dandy today.", 'neg'),
    ("I feel amazing!", 'pos'),
    ('Gary is a friend of mine.', 'pos'),
    ("I can't believe I'm doing this.", 'neg')
]

classifier = NaiveBayesClassifier(train)

class WordListTest(TestCase):

    def setUp(self):
        self.words = 'Beautiful is better than ugly'.split()
        self.mixed = ['dog', 'dogs', 'blob', 'Blobs', 'text']

    def test_len(self):
        wl = tb.WordList(['Beautiful', 'is', 'better'])
        assert_equal(len(wl), 3)

    def test_slicing(self):
        wl = tb.WordList(self.words)
        first = wl[0]
        assert_true(isinstance(first, tb.Word))
        assert_equal(first, 'Beautiful')

def test_upper(self):
        blob = tb.TextBlob('lorem ipsum')
        assert_true(is_blob(blob.upper()))
        assert_equal(blob.upper(), tb.TextBlob('LOREM IPSUM'))

def test_sentiment_of_emoticons(self):
        b1 = tb.TextBlob("Faces have values =)")
        b2 = tb.TextBlob("Faces have values")
        assert_true(b1.sentiment[0] > b2.sentiment[0])

def test_senences_with_space_before_punctuation(self):
        text = "Uh oh. This sentence might cause some problems. : Now we're ok."
        b = tb.TextBlob(text)
        assert_equal(len(b.sentences), 3)

def test_discrete_sentiment(self):
        blob = tb.TextBlob("I feel great today.", analyzer=NaiveBayesAnalyzer())
        assert_equal(blob.sentiment[0], 'pos')

def test_define(self):
        w = tb.Word("hack")
        synsets = w.get_synsets(wn.NOUN)
        definitions = w.define(wn.NOUN)
        assert_equal(len(synsets), len(definitions))

def test_lemmatize(self):
        w = tb.Word("cars")
        assert_equal(w.lemmatize(), "car")
        w = tb.Word("wolves")
        assert_equal(w.lemmatize(), "wolf")
        w = tb.Word("went")
        assert_equal(w.lemmatize("v"), "go") # wordnet tagset
        assert_equal(w.lemmatize("VBD"), "go") # penn treebank tagset

def test_word_lists():
    animals = TextBlob("cat dog octopus ocropus")
    pluralized_words = animals.words.pluralize()
    corrected_words = animals.correct()
    word_ocropus = Word('ocropus')
    word_ocr_spellechecked = word_ocropus.spellcheck()
    word_mice = Word('mice')
    word_mice_lemmatized = word_mice.lemmatize()
    word_highest = Word('highest')
    word_highest_lemmatized = word_highest.lemmatize()

    # test word net simmilarities
    king_synsets = Word("king").get_synsets(pos=NOUN)
    king = Synset('king.n.01')
    queen = Synset('queen.n.02')
    man = Synset('man.n.01')
    wife = Synset('wife.n.01')
    woman = Synset('woman.n.01')
    octopus = Synset('octopus.n.01')
    kq_similarity = king.path_similarity(queen)
    km_similarity = king.path_similarity(man)

def test_setitem(self):
        wl = tb.WordList(['I', 'love', 'JavaScript'])
        wl[2] = tb.Word('Python')
        assert_equal(wl[2], tb.Word('Python'))

def test_slicing(self):
        wl = tb.WordList(self.words)
        first = wl[0]
        assert_true(isinstance(first, tb.Word))
        assert_equal(first, 'Beautiful')

        dogs = wl[0:2]
        assert_true(isinstance(dogs, tb.WordList))
        assert_equal(dogs, tb.WordList(['Beautiful', 'is']))