def __init__(self):
self.general = general.extractor()
self.microblogs = microblogs.extractor()
self.review = review.extractor()
self.comments = comments.extractor()
self.blogs_news = blogs_news.extractor()
self.scalerLargeText = scorer(1.0)
self.scalerMediumText = scorer(0.5)
self.scalerSmallText = scorer(1.5)
def __init__(self): self.general = general.extractor() self.microblogs = microblogs.extractor() self.review = review.extractor() self.comments = comments.extractor() self.blogs_news = blogs_news.extractor() self.scalerLargeText = scorer(1.0) self.scalerMediumText = scorer(0.5) self.scalerSmallText = scorer(1.5)
def getSentiment(self, text):
score_sentiment = scorer().scaleScore(
self.senti_object.getSentimentScore(text))
if score_sentiment <= -2:
return "Negative"
elif score_sentiment >= 2:
return "Positive"
else:
return "Neutral"
comment_tag_clean = comment_tag.dropna()
comments = comment_tag_clean['Comment']
tags = comment_tag_clean['tags']
S = extractor()
comments = np.array(comments)
tags = np.array(tags)
total_correct = 0
for i in range(0, comments.shape[0]):
# print i
predictions = []
true_values = []
current_pred = ""
from scoreScaler import scorer
score_sentiment = scorer().scaleScore(S.getSentimentScore(comments[i]))
if score_sentiment <= -2:
predictions.append("untrustworthy")
current_pred = "untrustworthy"
elif score_sentiment >= 2:
predictions.append("trustworthy")
current_pred = "trustworthy"
else:
predictions.append("mixed")
current_pred = "mixed"
true_values.append(tags[i])
print current_pred, tags[i], score_sentiment
if current_pred == tags[i]:
total_correct = total_correct + 1
print comment_tag_clean.shape[0], total_correct
comment_tag_clean = comment_tag.dropna()
comments = comment_tag_clean['Comment']
tags = comment_tag_clean['tags']
S = extractor()
comments = np.array(comments)
tags = np.array(tags)
total_correct = 0
for i in range(0,comments.shape[0]):
# print i
predictions = []
true_values = []
current_pred = ""
from scoreScaler import scorer
score_sentiment = scorer().scaleScore(S.getSentimentScore(comments[i]))
if score_sentiment <= -2:
predictions.append("untrustworthy")
current_pred = "untrustworthy"
elif score_sentiment >= 2:
predictions.append("trustworthy")
current_pred = "trustworthy"
else:
predictions.append("mixed")
current_pred = "mixed"
true_values.append(tags[i])
print current_pred, tags[i],score_sentiment
if current_pred == tags[i]:
total_correct = total_correct + 1
print comment_tag_clean.shape[0], total_correct
def __init__(self):
self.SentiModel = self.load_obj("_model")
self.ch2 = self.load_obj("_feature_selector")
self.vectorizer = self.load_obj("_vectorizer")
self.Scorer = scorer(1.0)
def __init__(self):
self.SentiModel = self.load_obj("_model")
self.ch2 = self.load_obj("_feature_selector")
self.vectorizer = self.load_obj("_vectorizer")
self.Scorer = scorer(1.0)
