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A distantly supervised approach for enriching product graphs with user opinions

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Abstract

Product Graphs (PGs) are knowledge graphs that structure the relationship of products and their characteristics. They have become very popular lately due to their potential to enable AI-related tasks in e-commerce. With the rise of social media, many dynamic and subjective information on products and their characteristics became widely available, creating an opportunity to aggregate such information to PGs. In this paper, we propose a method called PGOpi (Product Graph enriched with Opinions), whose goal is to enrich existing PGs with subjective information extracted from reviews written by customers. PGOpi uses a deep learning model to map opinions extracted from user reviews to nodes in the PG corresponding to targets of these opinions. To alleviate manual labor dependency for training the model, we devise a distant supervision strategy based on word embeddings. We have performed an extensive experimental evaluation on five product categories of two representative real-world datasets. The proposed unsupervised approach achieves superior micro F1 score over more complex unsupervised models. It also presents comparable results to a fully-supervised model.

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Data Availability

The datasets generated during and/or analysed during the current study are available in the Google Drive repository: http://tiny.cc/rk0wtz. Additionally, the code for the experiments is available in Github repository: https://github.com/guardiaum/PGOpi.

Notes

  1. http://www.pewinternet.org/2016/12/19/online-shopping-and-e-commerce

  2. P roduct G raph enriched with Opi nions

  3. These are fictitious smartphone models with features similar to real ones.

  4. https://www.amazon.com/

  5. http://www.bestbuy.com

  6. http://tiny.cc/rk0wtz

  7. https://github.com/guardiaum/PGOpi

References

  • Bergstra, J. S., Bardenet, R., Bengio, Y., & Kégl, B. (2011). Algorithms for hyper-parameter optimization. In Advances in neural information processing systems, pp. 2546–2554.

  • Bergstra, J., Yamins, D., & Cox, D. (2013). Making a science of model search: Hyperparameter optimization in hundreds of dimensions for vision architectures. In Intl. Conf. on machine learning, pp. 115–123.

  • Dong, X. L. (2018). Challenges and innovations in building a product knowledge graph. In Proc. of the 24th ACM SIGKDD intl. conf. on knowledge discovery & data mining, pp. 2869–2869.

  • de Melo, T., da Silva, A. S., de Moura, E. S., & Calado, P. (2019). Opinionlink: Leveraging user opinions for product catalog enrichment. Information Processing & Management, 56(3), 823–843.

    Article  Google Scholar 

  • Guo, S., Wang, Q., Wang, L., Wang, B., & Guo, L. (2018). Knowledge graph embedding with iterative guidance from soft rules. In 32Th AAAI conf. on artificial intelligence, pp. 4816–4823.

  • Halevy, A. Y. (2019). The ubiquity of subjectivity. IEEE Data Eng. Bull., 42(1), 6–9.

    MathSciNet  Google Scholar 

  • Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural Computation, 9(8), 1735–1780.

    Article  Google Scholar 

  • Hu, M., & Liu, B. (2004). Mining and summarizing customer reviews. In Proc. of the 10th ACM SIGKDD Intl. Conf. on knowledge discovery & data mining, pp. 168–177.

  • Kasneci, G., Suchanek, F. M., Ifrim, G., Ramanath, M., & Weikum, G. (2008). Naga: Searching and ranking knowledge. In Proc. of the 24th intl. conf. on data engineering, pp. 953–962.

  • Kim, H. (2017). Towards a sales assistant using a product knowledge graph. Web Semantics: Science Services and Agents on the World Wide Web, 46, 14–19.

    Article  Google Scholar 

  • Kingma, D. P., & Ba, J. (2015). Adam: a method for stochastic optimization. In 3Rd Intl. Conf. on learning representations.

  • Kobren, A., Barrio, P., Yakhnenko, O., Hibschman, J., & Langmore, I. (2019). Constructing high precision knowledge bases with subjective and factual attributes. In Proc. of the 25th ACM SIGKDD intl. Conf. on knowledge discovery & data mining, pp. 2050–2058.

  • LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436–444.

    Article  Google Scholar 

  • Li, F. -L., Chen, H., Xu, G., Qiu, T., Ji, F., Zhang, J., & Chen, H. (2020). Alimekg: Domain knowledge graph construction and application in e-commerce. In Proceedings of the 29th ACM intl. conf. on information & knowledge management, pp. 2581–2588.

  • Li, Y., Feng, A., Li, J., Mumick, S., Halevy, A., Li, V., & Tan, W. -C. (2019). Subjective databases. Proc. VLDB Endow., 12(11), 1330–1343.

    Article  Google Scholar 

  • Li, Q., Jin, Z., Wang, C., & Zeng, D. D. (2016). Mining opinion summarizations using convolutional neural networks in chinese microblogging systems. Knowledge-Based Systems, 107, 289–300.

    Article  Google Scholar 

  • Lin, T. -Y., Goyal, P., Girshick, R., He, K., & Dollár, P. (2017). Focal loss for dense object detection. In Proc. of the IEEE intl. Conf. on computer vision, pp. 2980–2988.

  • Liu, M., Fang, Y., Choulos, A. G., Park, D. H., & Hu, X. (2017). Product review summarization through question retrieval and diversification. Information Retrieval Journal, 20(6), 575–605.

    Article  Google Scholar 

  • Luo, Z., Huang, S., & Zhu, K. Q. (2019). Knowledge empowered prominent aspect extraction from product reviews. Information Processing & Management, 56(3), 408–423.

    Article  Google Scholar 

  • McAuley, J., & Yang, A. (2016). Addressing complex and subjective product-related queries with customer reviews. In Proc. of the 25th intl. Conf. on world wide web, pp. 625–635.

  • McAuley, J., et al. (2015). Image-based recommendations on styles and substitutes. In Proc. of the 38th intl. ACM SIGIR conf. on research and development in information retrieval, pp. 43–52.

  • Mikolov, T., Sutskever, I., Chen, K., Corrado, G. S., & Dean, J. (2013). Distributed representations of words and phrases and their compositionality. In Advances in neural information processing systems, pp. 3111–3119.

  • Moussa, M. E., Mohamed, E. H., & Haggag, M. H. (2018). A survey on opinion summarization techniques for social media. Future Computing and Informatics Journal, 3(1), 82–109.

    Article  Google Scholar 

  • Poria, S., Cambria, E., Ku, L., Gui, C., & Gelbukh, A. F. (2014). A rule-based approach to aspect extraction from product reviews. In Proc. of the second workshop on natural language processing for social media, pp. 28–37.

  • Rana, T. A., & Cheah, Y. -N. (2016). Aspect extraction in sentiment analysis: comparative analysis and survey. Artificial Intelligence Review, 46(4), 459–483.

    Article  Google Scholar 

  • Rospocher, M., et al. (2016). Building event-centric knowledge graphs from news. Web Semantics: Science and Services and Agents on the World Wide Web, 37, 132–151.

    Article  Google Scholar 

  • Schuster, M., & Paliwal, K. (1997). Networks bidirectional recurrent neural. IEEE Transactions on Signal Processing, 45, 2673–2681.

    Article  Google Scholar 

  • Song, Q., Wu, Y., Lin, P., Dong, L. X., & Sun, H. (2018). Mining summaries for knowledge graph search. IEEE Transactions on Knowledge and Data Engineering, 30(10), 1887–1900.

    Article  Google Scholar 

  • Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., & Salakhutdinov, R. (2014). Dropout: a simple way to prevent neural networks from overfitting. The Journal of Machine Learning Research, 15(1), 1929–1958.

    MathSciNet  MATH  Google Scholar 

  • Takamatsu, S., Sato, I., & Nakagawa, H. (2012). Reducing wrong labels in distant supervision for relation extraction. In Proc. of the 50th annual meeting of the ACL, pp. 721–729.

  • Tubishat, M., Idris, N., & Abushariah, M. (2021). Explicit aspects extraction in sentiment analysis using optimal rules combination. Future Generation Computer Systems, 114, 448–480.

    Article  Google Scholar 

  • Wang, L., & Ling, W. (2016). Neural network-based abstract generation for opinions and arguments. In Proc. of conf. of the north american chapter of the ACL: Human language technologies, pp. 47–57.

  • Wu, H., Gu, Y., Sun, S., & Gu, X. (2016). Aspect-based opinion summarization with convolutional neural networks. In Intl. Joint conf. on neural networks, pp. 3157–3163.

  • Xu, F., Pan, Z., & Xia, R. (2020). E-commerce product review sentiment classification based on a naïve Bayes continuous learning framework. Information Processing & Management, 57.

  • Xu, D., Ruan, C., Korpeoglu, E., Kumar, S., & Achan, K. (2020). Product knowledge graph embedding for e-commerce. In Proc. of the 13th intl. conf. on web search and data mining, pp. 672–680.

  • Yang, M., Qu, Q., Shen, Y., Liu, Q., Zhao, W., & Zhu, J. (2018). Aspect and sentiment aware abstractive review summarization. In Proc. of the 27th intl. conf. on computational linguistics, pp. 1110–1120.

  • Zhang, M., Fan, B., Zhang, N., Wang, W., & Fan, W. (2021). Mining product innovation ideas from online reviews. Information Processing & Management, 58.

  • Zheng, G., Mukherjee, S., Dong, X. L., & Li, F. (2018). Opentag: Open attribute value extraction from product profiles. In Proc. of the 24th ACM SIGKDD intl. conf. on knowledge discovery & data mining, pp. 1049–1058.

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Acknowledgements

We gratefully acknowledge the support of NVIDIA Corporation with the donation of the Tesla Titan Xp GPU used for this research.

Funding

This work was supported or partially supported by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) under funding grants No. 8882.347588/2019-01, 88887.130299/2017-01, and financial code 001; by the Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) funding grant No. 307248/2019-4; by the project MMBIAS (FAPESP MCTIC / CGI, 2020/05173-4); by the Brazilian funding agency FAPEAM-POSGRAD 2020 (Resolution 002/2020); and by the Gratificação de Produtividade Acadêmica (GPA) of the Universidade Estadual do Amazonas (Portaria 086/2021).

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Authors and Affiliations

  1. Centro de Informática, Universidade Federal de Pernambuco, Recife, Pernambuco, Brasil

    Johny Moreira & Luciano Barbosa

  2. Escola Superior de Tecnologia, Universidade do Estado do Amazonas, Manaus, Amazonas, Brasil

    Tiago de Melo

  3. Instituto de Computação, Universidade Federal do Amazonas, Manaus, Amazonas, Brasil

    Altigran da Silva

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  1. Johny Moreira
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  2. Tiago de Melo
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  4. Altigran da Silva
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Contributions

Johny Moreira: Conceptualization, Methodology, Software, Investigation, Validation, Formal analysis, Writing, Data Curation Tiago de Melo: Conceptualization, Methodology, Validation, Formal analysis, Writing, Data Curation, Supervision Luciano Barbosa: Conceptualization, Methodology, Validation, Formal analysis, Writing, Supervision Altigran da Silva: Conceptualization, Methodology, Validation, Formal analysis, Writing, Supervision.

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Correspondence to Johny Moreira.

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Moreira, J., de Melo, T., Barbosa, L. et al. A distantly supervised approach for enriching product graphs with user opinions. J Intell Inf Syst 59, 435–454 (2022). https://doi.org/10.1007/s10844-022-00717-5

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