Job description
.Build a RNN model, that will evaluate the sentiment of a text describing a product from the "mobile electronics" category. Perform model training on the data contained in the "amazon_us_reviews" file available in the TensorFlow library.Hint: to load the data from use the following commands:
import tensorflow_datasets as tfds
ds=tfds.load('amazon_us_reviews/Mobile_Electronics_v1_00',split='train',shuffle_files=True)
df=tfds.as_dataframe(ds)
df["Sentiment"]=df["data/star_rating"].apply(lambdascore:"positive"ifscore>=3else"negative")df['Sentiment']=df['Sentiment'].map({'positive':1,'negative':0})
df['short_review']=df['data/review_body'].str.decode("utf-8")df=dfhttp://"short_review","Sentiment"
As a result, df variable will containa data frame with the text to be assessed in the short_review column and the sentiment scoring in the Sentiment column.
complete code using the Keras library implementing, training and testing at least 1modelsolving thegiven problem.
