def test_get_tokens(self):
"""split tokens on word boundaries"""
util = Util()
tokens = util.get_tokens("i can't find a new-york t-shirt, nor find a stationary shop.")
expected = ['i', "can't", 'find', 'a', 'new', 'york', 't', 'shirt', 'nor', 'find', 'a', 'stationary', 'shop']
print(tokens)
self.assertTrue(tokens == expected)
def test_filter_alnum(self):
""" filter out non-alnum """
util = Util()
filtered = util.filter_alnum("i , 123 - apt12".split())
expected = ['i', "123", 'apt12']
print(filtered)
self.assertTrue(filtered == expected)
def compute(self, sentence):
from main import Util
util = Util()
tokens = util.get_tokens(sentence)
total_positive = 0
count_positive = 0
total_negative = 0
count_negative = 0
total_words = 0
for token in tokens:
rating = self.ratings.get(token.lower(), 0)
if rating < 0:
total_negative += rating
count_negative += 1
elif rating > 0:
total_positive += rating
count_positive += 1
total_words += 1
positive_pct = 0
negative_pct = 0
positive_avg = 0
negative_avg = 0
if total_words > 0:
positive_pct = (count_positive / float(total_words)) * 100
negative_pct = (count_negative / float(total_words)) * 100
if count_positive > 0:
positive_avg = total_positive / float(count_positive)
if count_negative > 0:
negative_avg = total_negative / float(count_negative)
"""
add positive and negative averages together
e.g. +5 + -1 = +4 marks it as overly positive
+1 + -5 = -4 marks it as overly negative
+1 + -1 = 0 marks it as neutral
+5 + -5 = 0 marks it as neutral (this is confusing)
"""
total = positive_avg + negative_avg
if total >= 2.5:
overall_sentiment = "overly_positive"
elif total >= 0.5:
overall_sentiment = "positive"
elif total <= -0.5:
overall_sentiment = "negative"
elif total <= -2.5:
overall_sentiment = "overly_negative"
else:
overall_sentiment = "neutral"
ssc_values = SscValues(positive_pct, negative_pct, positive_avg, negative_avg, overall_sentiment)
return ssc_values
def test_ratings_sum(self):
"sum ratings for some words"
afinn = Afinn("AFINN-111.txt")
sentence = "Rainy day but still in a good mood"
util = Util()
aggregate_sentiment = 0
for word in util.get_tokens(sentence):
if afinn.has_rating(word):
aggregate_sentiment += afinn.get_rating(word)
self.assertTrue(aggregate_sentiment == 2)
