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import numpy as np
from vaderSentiment.vaderSentiment import SentimentIntensityAnalyzer
from textblob import TextBlob
def extract(corpus_instance):
"""
Extracts single "contrast" feature from a single corpus instance.
Returns numpy array of size 1.
"""
review = corpus_instance["REVIEW"]
stars = float(corpus_instance["STARS"])
#sent = get_sent_vader(review)
sent = get_sent_textblob(review)
if (sent <= 0.0 and stars > 3.0) or (sent > 0.0 and stars < 3.0):
return np.array([1])
else:
return np.array([0])
def get_sent_vader(string):
analyser = SentimentIntensityAnalyzer()
sent = analyser.polarity_scores(string)
return sent['compound']
def get_sent_textblob(string):
blob = TextBlob(string)
return blob.sentiment.polarity
def confusion_matrix(true_labels, predicted_labels):
matrix = np.zeros(shape=(2, 2))
for true, pred in zip(true_labels, predicted_labels):
matrix[true][pred] += 1
return matrix