Feature Relevance Analysis of Product Reviews to Support Online Shopping-Reference-Cited by-同舟云学术

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Feature Relevance Analysis of Product Reviews to Support Online Shopping

Published:2022 Issue: Volume: Page:441-446
ISSN:0302-9743
Container-title:Information Integration and Web Intelligence
language:
Short-container-title:

Author:

Yamaguchi Fumiya,Dollack Felix,Ueda Mayumi,Nakajima Shinsuke

Publisher

Springer Nature Switzerland

Link

https://link.springer.com/content/pdf/10.1007/978-3-031-21047-1_40

Reference9 articles.

1. 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)

2. 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

3. Haque, T.U., et al.: Sentiment analysis on large scale Amazon product reviews. In: IEEE International Conference on Innovative Research and Development (2018)

4. 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)

5. 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)

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. A Product Feature Mentioned Timestamp Extraction Method in Review Videos for Online Shopping;2024 International Conference on Computing, Networking and Communications (ICNC);2024-02-19

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