Sentiment Class

Inheritance

nimbusml.internal.core.feature_extraction.text._sentiment.Sentiment

Sentiment

nimbusml.base_transform.BaseTransform

Sentiment

sklearn.base.TransformerMixin

Sentiment

Constructor

Sentiment(columns=None, **params)

Parameters

Examples

   ###############################################################################
   # Sentiment: pre-trained transform to analyze sentiment of free-form
   # text.
   import pandas
   from nimbusml.feature_extraction.text import 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!!",
       "The best ever!! It was amazingly good and super fast",
       "I wish I had gone earlier, it was that great",
       "somewhat dissapointing. I'd probably wont try again",
       "Never visit again... rascals!"]))

   analyze = Sentiment() << 'review'

   # No need to fit any real data, just a dummy call to fit() to ensure the
   # column name 'review' is present when transform() is invoked

   # Skip until ML.NET resolve the resouce issue with Sentiment transform
   # y = analyze.fit_transform(customer_reviews)

   # View the sentiment scores!!
   # print(y)
   #     review
   # 0  0.461790
   # 1  0.960192
   # 2  0.310344
   # 3  0.965281
   # 4  0.409665
   # 5  0.492397
   # 6  0.332535

Remarks

The Sentiment transform returns the probability that the sentiment of a natural text is positive. The model was trained with the WordEmbedding and NGramFeaturizer on Twitter sentiment data, similarly to the sentiment analysis part of the Text Analytics cognitive service. The transform outputs a score between 0 and 1 as a sentiment prediction (where 0 is a negative sentiment and 1 is a positive sentiment). Currently it supports only English.

Methods

get_params

get_params(deep=False)