Multi-class Review Rating Classification using Deep Recurrent Neural Network

Abstract

This paper presents a gated-recurrent-unit (GRU) based recurrent neural network (RNN) architecture titled as DSWE-GRNN for multi-class review rating classification problem. Our model incorporates domain-specific word embeddings and does not depend on the reviewer’s information because we usually don’t have many reviews from the same user to measure the leniency of the user towards a specific sentiment. The RNN based architecture captures the hidden contextual information from the domain-specific word embeddings to effectively and efficiently train the model for review rating classification. In this work, we also demonstrate that downsampling technique for data balancing can be very effective for the model’s performance. We have evaluated our model over two datasets i.e IMDB dataset and the Hotel Reviews dataset. The results demonstrate that our model’s performance (accuracy) is comparable with or even better than the four baseline methods used for sentiment classification in literature.

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Acknowledgements

Authors are thankful to their institution (Department of Computer Science at Hafiz Hayat Campus, University of Gujrat, Gujrat, Pakistan) for providing us the platform for conducting the research and analysis.

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Correspondence to Junaid Hassan.

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Hassan, J., Shoaib, U. Multi-class Review Rating Classification using Deep Recurrent Neural Network. Neural Process Lett 51, 1031–1048 (2020). https://doi.org/10.1007/s11063-019-10125-6

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Keywords

  • Sentiment analysis
  • Multi-class classification
  • Text classification
  • Deep neural networks
  • Review-rating classification