microsoftml.get_sentiment: Analisis sentimen

Penggunaan

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

Deskripsi

Menilai teks bahasa alami dan menilai probabilitas sentimennya positif.

Detail

get_sentiment Transformasi mengembalikan probabilitas bahwa sentimen teks alami positif. Saat ini hanya mendukung bahasa Inggris.

Argumen

Cols

String karakter atau daftar nama variabel untuk diubah. Jika dict, nama mewakili nama variabel baru yang akan dibuat.

karg

Argumen tambahan dikirim ke mesin komputasi.

Mengembalikan

Objek yang menentukan transformasi.

Lihat juga

featurize_text.

Contoh

'''
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