microsoftml.get_sentiment : analyse des sentiments

Article
05/23/2023

Usage

microsoftml.get_sentiment(cols: [str, dict, list], **kargs)

Description

Score du texte en langage naturel et évalue la probabilité que les sentiments soient positifs.

Détails

La transformation get_sentiment retourne la probabilité que le sentiment d’un texte naturel soit positif. Prend en charge uniquement la langue anglaise.

Arguments

cols

Chaîne de caractères ou liste de noms de variables à transformer. Si dict, les noms représentent les noms des nouvelles variables à créer.

kargs

Arguments supplémentaires envoyés au moteur de calcul.

Retours

Objet définissant la transformation.

Voir aussi

featurize_text.

Exemple

'''
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)

Sortie :

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

Partager via