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Modeling different effects of user and product attributes on review sentiment classification

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Abstract

As a fundamental task, review sentiment classification aims to predict a user’s overall sentiment in a review about a product. Recent studies have proven critical effects of user and product attributes on this task. However, they usually adopt the same way to incorporate these two attributes, which does not fully consider their different effects and thus is not capable of leveraging them effectively. To address this issue, we propose a simple and effective review sentiment classification model based on hierarchical attention network, where different effects of user and product attributes are respectively captured via fusion modules and attention mechanisms. We further propose a training framework based on mutual distillation to fully capture the individual effects of user and product attributes. Specifically, two auxiliary models with only the user or product attribute are introduced to benefit our model. During the joint training process, our model and the auxiliary models boost each other via mutual knowledge distillation in an iterative manner. On the benchmark IMDB and Yelp datasets, our model significantly outperforms competitive baselines with a 1.6% improvement in average accuracy. When BERT embeddings are used as inputs, our model still performs much better than recent BERT-enhanced baselines.

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Data Availability and Access

The datasets generated during and/or analysed during the current study are publicly available on here. Note that the used Yelp 2013, Yelp 2014 and IMDB datasets are provided by authors of ACL 2015 Paper “Learning Semantic Representations of Users and Products for Document Level Sentiment Classification”. The datasets generated during and/or analysed during the current study are also available from the corresponding author on reasonable request.

Notes

  1. In the experimental datasets, the anonymized user ID and product ID are provided for each review, without any other information of them.

  2. This statement can be supported by the experimental results in Table 5, where CFeat-MLoss (with an additional loss for each representation) achieves substantially better performance than CFeat (concatenating three review representations directly). Similar experimental phenomena have also been reported in [52] and [60].

  3. In our experiments, we use a learnable vector to replace the masked product or user attribute. Note that we mask an attribute to mimic real situations where it is absent.

  4. The \(L_2\) loss [5], cross-entropy loss [20], and KL divergence loss [61] are typically used in knowledge transfer/distillation approaches. In our preliminary experiments, we find the KL divergence loss work slightly better than the other two losses.

  5. http://www.yelp.com/dataset_challenge

  6. https://stanfordnlp.github.io/CoreNLP/

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Acknowledgements

We would like to thank all the reviewers for their constructive and helpful suggestions on this paper. This work was supported in part by the National Natural Science Foundation of China (Nos. 62266017, 62166018 and 61861032), and the Natural Science Foundation of Jiangxi Province of China (No. 20232BAB202050).

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Authors and Affiliations

  1. School of Software, East China Jiaotong University, Nanchang, 330013, Jiangxi, China

    Changxing Wu, Liuwen Cao & Jiayu Chen

  2. School of Information Engineering, Nanchang Institute of Technology, Nanchang, 330099, Jiangxi, China

    Yuanyun Wang

  3. School of Informatics, Xiamen University, Xiamen, 361005, Fujian, China

    Jinsong Su

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  1. Changxing Wu
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Contributions

Changxing Wu: Conceptualization, Methodology, Software, Writing - Original Draft Liuwen Cao: Software, Validation, Data Curation Jiayu Chen: Software, Validation, Data Curation Yuanyun Wang: Conceptualization, Methodology Jinsong Su: Conceptualization, Methodology, Writing - Original Draft

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Correspondence to Changxing Wu.

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Wu, C., Cao, L., Chen, J. et al. Modeling different effects of user and product attributes on review sentiment classification. Appl Intell 54, 835–850 (2024). https://doi.org/10.1007/s10489-023-05236-6

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