class TestNaiveBayesAnalyzer(unittest.TestCase):
def setUp(self):
self.analyzer = NaiveBayesAnalyzer()
def test_kind(self):
assert_equal(self.analyzer.kind, DISCRETE)
@attr('slow')
def test_analyze(self):
p1 = 'I feel great this morning.'
n1 = 'This is a terrible car.'
p1_result = self.analyzer.analyze(p1)
assert_equal(p1_result[0], 'pos')
assert_equal(self.analyzer.analyze(n1)[0], 'neg')
# The 2nd item should be the probability that it is positive
assert_true(isinstance(p1_result[1], float))
# 3rd item is probability that it is negative
assert_true(isinstance(p1_result[2], float))
assert_about_equal(p1_result[1] + p1_result[2], 1)
class TestNaiveBayesAnalyzer(unittest.TestCase):
def setUp(self):
self.analyzer = NaiveBayesAnalyzer()
def test_kind(self):
assert_equal(self.analyzer.kind, DISCRETE)
@attr('slow')
def test_analyze(self):
p1 = 'I feel great this morning.'
n1 = 'This is a terrible car.'
p1_result = self.analyzer.analyze(p1)
assert_equal(p1_result[0], 'pos')
assert_equal(self.analyzer.analyze(n1)[0], 'neg')
# The 2nd item should be the probability that it is positive
assert_true(isinstance(p1_result[1], float))
# 3rd item is probability that it is negative
assert_true(isinstance(p1_result[2], float))
assert_about_equal(p1_result[1] + p1_result[2], 1)
def __init__(self):
# create custom components
self.naive_bayes_analyzer = NaiveBayesAnalyzer()
self.conll_extractor = ConllExtractor()
self.nltk_tagger = NLTKTagger()
self.perceptron_tagger = PerceptronTagger()
if DEV_ENV:
return
# train all components (default and custom)
text = 'TextBlob blobs great!'
default_blob = TextBlob(text)
default_blob.sentiment
default_blob.noun_phrases
default_blob.pos_tags
custom_blob = TextBlob(text,
analyzer=self.naive_bayes_analyzer,
np_extractor=self.conll_extractor,
pos_tagger=self.nltk_tagger)
custom_blob.sentiment
custom_blob.noun_phrases
custom_blob.pos_tags
custom2_blob = TextBlob(text, pos_tagger=self.perceptron_tagger)
custom2_blob.pos_tags
def test_override_analyzer(self):
b = tb.Blobber(analyzer=NaiveBayesAnalyzer())
blob = b("How now?")
blob2 = b("Brown cow")
assert_true(isinstance(blob.analyzer, NaiveBayesAnalyzer))
assert_true(blob.analyzer is blob2.analyzer)
def test_can_get_subjectivity_and_polarity_with_different_analyzer(self):
blob = tb.TextBlob("I love this car.", analyzer=NaiveBayesAnalyzer())
pattern = PatternAnalyzer()
assert_equal(blob.polarity, pattern.analyze(str(blob))[0])
assert_equal(blob.subjectivity, pattern.analyze(str(blob))[1])
def test_discrete_sentiment(self):
blob = tb.TextBlob("I feel great today.",
analyzer=NaiveBayesAnalyzer())
assert_equal(blob.sentiment[0], 'pos')
def test_can_use_different_sentanalyzer(self):
blob = tb.TextBlob("I love this car", analyzer=NaiveBayesAnalyzer())
assert_true(isinstance(blob.analyzer, NaiveBayesAnalyzer))
def setUp(self):
self.analyzer = NaiveBayesAnalyzer()
def setUp(self):
self.analyzer = NaiveBayesAnalyzer()
