Utility Scoring of Product Reviews

• 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.

• Dataset

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

• Classification

  • 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

• Conclusions

  • 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