Product Review Ranking in e-Commerce using Urgency Level Classification Approach

Authors

  • Hamdi Ahmad Zuhri School of Electrical Engineering and Informatics, Institut Teknologi Bandung, Indonesia, Indonesia
  • Nur Ulfa Maulidevi School of Electrical Engineering and Informatics, Institut Teknologi Bandung, Indonesia and PUI-PT AI-VLB (Artificial Intelligence for Vision, Natural Language Processing & Big Data Analytics), Indonesia, Indonesia

DOI:

https://doi.org/10.15575/join.v5i2.612

Keywords:

Product review, Text classification, Text ranking

Abstract

Review ranking is useful to give users a better experience. Review ranking studies commonly use upvote value, which does not represent urgency, and it causes problems in prediction. In contrast, manual labeling as wide as the upvote value range provides a high bias and inconsistency. The proposed solution is to use a classification approach to rank the review where the labels are ordinal urgency class. The experiment involved shallow learning models (Logistic Regression, Naïve Bayesian, Support Vector Machine, and Random Forest), and deep learning models (LSTM and CNN). In constructing a classification model, the problem is broken down into several binary classifications that predict tendencies of urgency depending on the separation of classes. The result shows that deep learning models outperform other models in classification dan ranking evaluation. In addition, the review data used tend to contain vocabulary of certain product domains, so further research is needed on data with more diverse vocabulary.

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Published

2020-12-18

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