Utility is the usefulness or reliability of a given review.

Utility score is the score given to a review based on its usefulness from 0 to 1.

• We downloaded reviews and related data in 22 different domains like Electronics, Clothing, Sports,Video games etc from amazon product data

• Classify a given review into one of the 22 categories using

• Neural Network Architecture

  • Embedding layer with the size of vocabulary.
  • 1-D convolutional layer with 32 filters of size 3 with relu activation.
  • Max pooling 1-D layer with pool size 2
  • Fully connected layer with relu activation function.
  • Fully connected layer with 22 nodes and softmax classifier.

• Voted Classifier

  • Ensemble of 5 models (SVM, Decision Tree, Naive Bayes etc) to classify a given review using its TF-IDF vector.

• Adjectives

  • Generated adjectives for each category using a seed set and recursively adding all its synonyms. The category with maximum count is the class of the given review.

• Learning Algorithms

  • Support Vector Regression (SVR)
  • Simple linear regression (SLR)

• Comparision

• Classification
  • Accuracy in classification is highest in the case of Voted Classifier.
  • Voted classifier has the advantage of testing a review with 5 different classifiers.
  • Neural networks also works good but less accurate than voted classifier.
  • The naive method of counting number of matches in adjectives does not work well as there is no learning based in this method and also it is ambiguous