def test_sentiment(self):
# Assert < 0 for negative adjectives and > 0 for positive adjectives.
self.assertTrue(nl.sentiment("geweldig")[0] > 0)
self.assertTrue(nl.sentiment("verschrikkelijk")[0] < 0)
# Assert the accuracy of the sentiment analysis.
# Given are the scores for 3,000 book reviews.
# The baseline should increase (not decrease) when the algorithm is modified.
from pattern.db import Datasheet
from pattern.metrics import test
reviews = []
for score, review in Datasheet.load(os.path.join(PATH, "corpora", "polarity-nl-bol.com.csv")):
reviews.append((review, int(score) > 0))
A, P, R, F = test(lambda review: nl.positive(review), reviews)
self.assertTrue(A > 0.80)
self.assertTrue(P > 0.77)
self.assertTrue(R > 0.85)
self.assertTrue(F > 0.81)
print "pattern.nl.sentiment()"
def test_sentiment(self):
# Assert < 0 for negative adjectives and > 0 for positive adjectives.
self.assertTrue(nl.sentiment("geweldig")[0] > 0)
self.assertTrue(nl.sentiment("verschrikkelijk")[0] < 0)
# Assert the accuracy of the sentiment analysis.
# Given are the scores for 3,000 book reviews.
# The baseline should increase (not decrease) when the algorithm is modified.
from pattern.db import Datasheet
from pattern.metrics import test
reviews = []
for score, review in Datasheet.load(os.path.join(PATH, "corpora", "polarity-nl-bol.com.csv")):
reviews.append((review, int(score) > 0))
A, P, R, F = test(lambda review: nl.positive(review), reviews)
self.assertTrue(A > 0.80)
self.assertTrue(P > 0.77)
self.assertTrue(R > 0.85)
self.assertTrue(F > 0.81)
print "pattern.nl.sentiment()"
