microsoftml.get_sentiment: Sentiment analysis
microsoftml.get_sentiment(cols: [str, dict, list], **kargs)
Scores natural language text and assesses the probability the sentiments are positive.
The get_sentiment transform returns the probability
that the sentiment of a natural text is positive. Currently supports
only the English language.
A character string or list of variable names to transform. If
dict, the names represent the names of new variables to be created.
Additional arguments sent to compute engine.
An object defining the transform.
'''
Example with get_sentiment and rx_logistic_regression.
'''
import numpy
import pandas
from microsoftml import rx_logistic_regression, rx_featurize, rx_predict, get_sentiment
# Create the data
customer_reviews = pandas.DataFrame(data=dict(review=[
"I really did not like the taste of it",
"It was surprisingly quite good!",
"I will never ever ever go to that place again!!"]))
# Get the sentiment scores
sentiment_scores = rx_featurize(
data=customer_reviews,
ml_transforms=[get_sentiment(cols=dict(scores="review"))])
# Let's translate the score to something more meaningful
sentiment_scores["eval"] = sentiment_scores.scores.apply(
lambda score: "AWESOMENESS" if score > 0.6 else "BLAH")
print(sentiment_scores)
Output:
Beginning processing data.
Rows Read: 3, Read Time: 0, Transform Time: 0
Beginning processing data.
Elapsed time: 00:00:02.4327924
Finished writing 3 rows.
Writing completed.
review scores eval
0 I really did not like the taste of it 0.461790 BLAH
1 It was surprisingly quite good! 0.960192 AWESOMENESS
2 I will never ever ever go to that place again!! 0.310344 BLAH