def run_sentiment_analysis(df):
""" Adds score, magnitude and discrete_sentiment to df using the Google Could Sentiment API
Note that the function can sometimes run into rate-limit restrictions, which is why
the calls are wrapped in a while loop, to ensure that the API is called for all rows.
"""
sentiment_score = {}
sentiment_magnitude = {}
for i, row in df.iterrows():
while True:
try:
text = row.title + " " + row.question_content
score, magnitude = gc_sentiment(text, type='HTML')
sentiment_score[row.question_id] = score
sentiment_magnitude[row.question_id] = magnitude
except (Forbidden, TooManyRequests) as e:
print(e)
print('Waiting 100 seconds due to rate-limit constraint')
time.sleep(100)
continue
break
df[u'score'] = df['question_id'].map(sentiment_score)
df[u'magnitude'] = df['question_id'].map(sentiment_magnitude)
df[u'discrete_sentiment'] = df.apply(lambda x: \
discretize_sentiment(x['score'],x['magnitude']), axis=1)
return (df)
def test_low_magnitude_positive_score(self):
score = 1
magnitude = 0.1
result = discretize_sentiment(score,
magnitude,
score_cutoff=0.2,
magnitude_cutoff=0.5)
self.assertEqual(result, 'neutral')
def test_high_magnitude_positive_score(self):
score = 1
magnitude = 1
result = discretize_sentiment(score,
magnitude,
score_cutoff=0.2,
magnitude_cutoff=0.5)
self.assertEqual(result, 'positive')