Abstract
The number of online shoppers has been increasing in recent years. Online shopping involves the risk that the purchased product may not be what was expected. Recently, the number of product review videos has also been increasing, and more users are using them as a reference because they provide a more accurate understanding of how the product is used than conventional reviews. With this development in mind, we have been developing a review video recommendation system to support online shopping. Our system helps users to know which product review videos they should watch. In this paper, we propose a review video feature analysis method, which is a necessary technology to realize the proposed system, and conduct two evaluation experiments to confirm the effectiveness of the proposed method. The results of the evaluation revealed that the proposed system received good ratings from the users, which confirmed the effectiveness of the proposed method.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
YouTube, https://www.youtube.com/.
References
Muhammad, A.N., et al.: Sentiment analysis of positive and negative of YouTube comments using Naïve Bayes - support vector machine (NBSVM) classifier. In: ICOMITEE: International Conference on Computer Science, Information Technology, and Electrical Engineering (2019)
Siersdorfer, S., Chelaru, S., Pedro, J.S., Altingovde, I.S., Nejdl, W.: Analyzing and mining comments and comment ratings on the social web. ACM Trans. Web 8(3), 1–39 (2014). Article 17
Haque, T.U., et al.: Sentiment analysis on large scale Amazon product reviews. In: IEEE International Conference on Innovative Research and Development (2018)
Basani, Y., et al.: Application of sentiment analysis on product review e-commerce. In: International Conference on Advance and Scientific Innovation (ICASI), vol. 1175 (2019)
Zhang, Z., et al.: Utility scoring of product reviews. In: The 15th ACM International Conference on Information and Knowledge Management (CIKM 2006), pp. 51–57 (2006)
Matsunami, Y., et al.: Explaining item ratings in cosmetic product reviews. In: IMECS 2016 (2016)
Scaffidi, C., et al.: Red Opal: product-feature scoring from reviews. In: EC 2007: The 8th ACM Conference on Electronic Commerce, pp. 182–191, June 2007
Ueda, M., et al.: A research on constructing evaluative expression dictionaries for cosmetics based on Word2Vec. In: iiWAS 2021, pp. 84–90 (2021)
Brooke, J.: SUS—a quick and dirty usability scale. In: Jordan, P.W., Thomas, B., Weerdmeester, B.A., McClelland, A.L. (eds.) Usability Evaluation in Industry. Taylor and Francis, London (1996)
Acknowledgements
This work was supported by multiple JSPS KAKENHI research grants (19K12243, 20H04293, 22K12281). It is also a product of research activity of the Institute of Advanced Technology and the Center for Sciences towards Symbiosis among Human, Machine and Data which was financially supported by a Kyoto Sangyo University Research Grant (M2001).
Author information
Authors and Affiliations
Corresponding authors
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Yamaguchi, F., Dollack, F., Ueda, M., Nakajima, S. (2022). Feature Relevance Analysis of Product Reviews to Support Online Shopping. In: Pardede, E., Delir Haghighi, P., Khalil, I., Kotsis, G. (eds) Information Integration and Web Intelligence. iiWAS 2022. Lecture Notes in Computer Science, vol 13635. Springer, Cham. https://doi.org/10.1007/978-3-031-21047-1_40
Download citation
DOI: https://doi.org/10.1007/978-3-031-21047-1_40
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-21046-4
Online ISBN: 978-3-031-21047-1
eBook Packages: Computer ScienceComputer Science (R0)