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Extraction and prioritization of product attributes using an explainable neural network

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Abstract

Identification of product attributes is an important matter in real-world business environments because customers generally make purchase decisions based on their evaluation of the attributes of the product. Numerous studies on product attribute extraction have been performed on the basis of user-generated textual reviews. However, most of them focused only on the attribute extraction process itself and not on the relative importance of the extracted attributes, which are critical information that can be utilized for the promotion or development of specification sheets. Thus, in this study, we focused on the development of an attribute set for a product by considering the relative importance of the extracted attributes. First, we extracted the aspects by utilizing convolutional neural network-based approaches and transfer learning. Second, we propose a novel approach, consisting of variants of the Gradient-weighted class activation mapping (Grad-CAM) algorithm, one of the explainable neural network frameworks, to capture the importance score of each extracted aspect. Using a sentimental prediction model, we calculated the weight of each aspect that affects the sentiment decision. We verified the performance of our proposed method by comparing the similarity of the product attributes that it extracted and their relative importance with the product attributes that customers consider to be the most important and by comparing the attributes used to develop the specification sheet of an existing major commercial site.

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References

  1. Bing L, Wong TL, Lam W (2016) Unsupervised extraction of popular product attributes from e-commerce web sites by considering customer reviews. ACM Trans Internet Technol (TOIT) 16(2):12

    Article  Google Scholar 

  2. Choi Y, Cardie C (2010) Hierarchical sequential learning for extracting opinions and their attributes. In: Proceedings of the ACL 2010 conference short papers. Association for Computational Linguistics, pp 269–274

  3. Fuchs C, Prandelli E, Schreier M (2010) The psychological effects of empowerment strategies on consumers’ product demand. J Mark 74(1):65–79

    Article  Google Scholar 

  4. van Ginneken B, Setio AA, Jacobs C, Ciompi F (2015) Off-the-shelf convolutional neural network features for pulmonary nodule detection in computed tomography scans. In: IEEE 12th international symposium on biomedical imaging (ISBI). IEEE, pp 286–289

  5. Huang S, Liu X, Peng X, Niu Z (2012) Fine-grained product features extraction and categorization in reviews opinion mining. In: IEEE 12th international conference on data mining workshops (ICDMW). IEEE, pp 680–686

  6. Jagarlamudi J, Daumé III H, Udupa R (2012) Incorporating lexical priors into topic models. In: Proceedings of the 13th conference of the European chapter of the association for computational linguistics. Association for Computational Linguistics, pp 204–213

  7. Jakob N, Gurevych I (2010) Extracting opinion targets in a single-and cross-domain setting with conditional random fields. In: Proceedings of the 2010 conference on empirical methods in natural language processing. Association for Computational Linguistics, pp 1035–1045

  8. Jamal A, Goode M (2001) Consumers’ product evaluation: a study of the primary evaluative criteria in the precious jewellery market in the UK. J Consum Behav 1(2):140–155

    Article  Google Scholar 

  9. Järvelin K, Kekäläinen J (2000) IR evaluation methods for retrieving highly relevant documents. In: Proceedings of the 23rd annual international ACM SIGIR conference on Research and development in information retrieval. ACM, pp 41–48

  10. Järvelin K, Kekäläinen J (2002) Cumulated gain-based evaluation of IR techniques. ACM Trans Inf Syst (TOIS) 20(4):422–446

    Article  Google Scholar 

  11. Jin W, Ho HH, Srihari RK (2009) A novel lexicalized hmm-based learning framework for web opinion mining. In: Proceedings of the 26th annual international conference on machine learning, pp 465–472

  12. Kaboli M (2017) A review of transfer learning algorithms. Ph.D. thesis, Technische Universität München

  13. Kangale A, Kumar SK, Naeem MA, Williams M, Tiwari MK (2016) Mining consumer reviews to generate ratings of different product attributes while producing feature-based review-summary. Int J Syst Sci 47(13):3272–3286

    Article  MATH  Google Scholar 

  14. Lee TY, Bradlow ET (2011) Automated marketing research using online customer reviews. J Mark Res 48(5):881–894

    Article  Google Scholar 

  15. Lee Y, Chung M, Cho S, Choi J (2019) Extraction of product evaluation factors with a convolutional neural network and transfer learning. Neural Proc Lett 50:149–164

    Article  Google Scholar 

  16. Li F, Han C, Huang M, Zhu X, Xia YJ, Zhang S, Yu H (2010) Structure-aware review mining and summarization. In: Proceedings of the 23rd international conference on computational linguistics. Association for Computational Linguistics, pp 653–661

  17. Li Z, Tang J (2015) Weakly supervised deep metric learning for community-contributed image retrieval. IEEE Trans Multimed 17(11):1989–1999

    Article  Google Scholar 

  18. Li Z, Tang J (2016) Weakly supervised deep matrix factorization for social image understanding. IEEE Trans Image Proc 26(1):276–288

    Article  MathSciNet  MATH  Google Scholar 

  19. Li Z, Tang J, Mei T (2018) Deep collaborative embedding for social image understanding. IEEE Trans Pattern Anal Mach Intell 41:2070–2083

    Article  Google Scholar 

  20. Lim S, Tucker CS (2016) A bayesian sampling method for product feature extraction from large-scale textual data. J Mech Des 138(6):061403

    Article  Google Scholar 

  21. Liu Q, Gao Z, Liu B, Zhang Y (2015) Automated rule selection for aspect extraction in opinion mining. IJCAI 15:1291–1297

    Google Scholar 

  22. Mikolov T, Chen K, Corrado G, Dean J (2013) Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781

  23. Pan SJ, Yang Q (2010) A survey on transfer learning. IEEE Trans Knowl Data Eng 22(10):1345–1359

    Article  Google Scholar 

  24. Poria S, Cambria E, Gelbukh A (2016) Aspect extraction for opinion mining with a deep convolutional neural network. Knowl Based Syst 108:42–49

    Article  Google Scholar 

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

  26. Qiu G, Liu B, Bu J, Chen C (2011) Opinion word expansion and target extraction through double propagation. Comput Linguist 37(1):9–27

    Article  Google Scholar 

  27. Quan C, Ren F (2014) Unsupervised product feature extraction for feature-oriented opinion determination. Inf Sci 272:16–28

    Article  Google Scholar 

  28. Ribeiro MT, Singh S, Guestrin C (2016) Why should I trust you? Explaining the predictions of any classifier. In: Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining. ACM, pp 1135–1144

  29. Samiee S (1994) Customer evaluation of products in a global market. J Int Bus Stud 25(3):579–604

    Article  Google Scholar 

  30. Selvaraju RR, Das A, Vedantam R, Cogswell M, Parikh D, Batra D (2016) Grad-cam: why did you say that? Visual explanations from deep networks via gradient-based localization. CoRR. abs/1610.02391, 7

  31. SemEval-2014: Semantic evaluation exercises. In: International workshop on semantic evaluation (SemEval-2014), Dublin, Ireland. https://alt.qcri.org/semeval2014

  32. Sharif Razavian A, Azizpour H, Sullivan J, Carlsson S (2014) CNN features off-the-shelf: an astounding baseline for recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition workshops, pp 806–813

  33. Socher R, Ganjoo M, Manning CD, Ng A (2013) Zero-shot learning through cross-modal transfer. In: Advances in neural information processing systems, pp 935–943

  34. Toh Z, Wang W (2014) DLIREC: aspect term extraction and term polarity classification system. In: Proceedings of the 8th international workshop on semantic evaluation (SemEval 2014), pp 235–240

  35. Wang T, Cai Y, Leung H, Lau RY, Li Q, Min H (2014) Productaspect extraction supervised with online domain knowledge. Knowl Based Syst 71:86–100

    Article  Google Scholar 

  36. Wang Y, Huang M, Zhao L et al (2016) Attention-based LSTM for aspect-level sentiment classification. In: Proceedings of the 2016 conference on empirical methods in natural language processing, pp 606–615

  37. Wang Y, Wang L, Li Y, He D, Liu TY (2013) A theoretical analysis of NDCG type ranking measures. In: Conference on learning theory, pp 25–54

  38. Xu K, Ba J, Kiros R, Cho K, Courville A, Salakhudinov R, Zemel R, Bengio Y (2015) Show, attend and tell: neural image caption generation with visual attention. In: International conference on machine learning, pp 2048–2057

  39. Xu X, Wang X, Li Y, Haghighi M (2017) Business intelligence in online customer textual reviews: understanding consumer perceptions and influential factors. Int J Inf Manag 37(6):673–683

    Article  Google Scholar 

  40. Yang B, Cardie C (2013) Joint inference for fine-grained opinion extraction. ACL 1:1640–1649

    Google Scholar 

  41. Zhong S (2005) Efficient online spherical k-means clustering. In: Proceedings of IEEE international joint conference on neural networks, vol 5. IEEE, pp 3180–3185

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Acknowledgements

This study was supported by Research Program funded by the SeoulTech.

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Authors and Affiliations

  1. Department of Industrial and Systems Engineering, Seoul National University of Science and Technology, 232 Gongneung-ro, Nowon-gu, Seoul, 01811, Korea

    Younghoon Lee

  2. Idea Lab, Corporate Business Strategy Office, LG Electronics, 128 Yeouidaero,Yeongdeungpo-gu, Seoul, 07336, Korea

    Jungmin Park

  3. Department of Industrial Engineering, Institute for Industrial Systems Innovation, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul, 08826, Korea

    Sungzoon Cho

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  1. Younghoon Lee
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Correspondence to Sungzoon Cho.

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Lee, Y., Park, J. & Cho, S. Extraction and prioritization of product attributes using an explainable neural network. Pattern Anal Applic 23, 1767–1777 (2020). https://doi.org/10.1007/s10044-020-00878-5

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