research-article

Cerebro: Novelty Detection in Product Reviews

Authors:

Ankita Sinha,

Vignesh SubrahmaniamAuthors Info & Claims

ICMLSC '20: Proceedings of the 4th International Conference on Machine Learning and Soft Computing

Pages 140 - 143

https://doi.org/10.1145/3380688.3380701

Published: 07 March 2020 Publication History

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Abstract

The recent boom in e-commerce has created active electronic communities where consumers share their thoughts about the product and the company. These reviews play a very important part in building customer opinion about the said item. For a popular product or service, there might be thousands of reviews, making it difficult for the customer to make an informed decision about the product. In this paper, we present a way to surface only those reviews that contain information relevant to the user. To address this problem, we try to surface out the reviews that are outliers to the general cluster of reviews during a particular time period.We are leveraging anomaly detection algorithms to achieve this.

References

[1]

Markus Breunig, Hans-Peter Kriegel, Raymond Ng, and Joerg Sander. 2000. LOF: Identifying Density-Based Local Outliers. ACM Sigmod Record 29, 93--104. https://doi.org/10.1145/342009.335388

Digital Library

Google Scholar

[2]

Zhangyu Cheng, Chengming Zou, and Jianwei Dong. 2019. Outlier detection using isolation forest and local outlier factor. 161--168. https://doi.org/10.1145/3338840.3355641

Digital Library

Google Scholar

[3]

Jesse Davis and Mark Goadrich. 2006. The Relationship Between Precision-Recall and ROC Curves. Proceedings of the 23rd International Conference on Machine Learning, ACM 06. https://doi.org/10.1145/1143844.1143874

Digital Library

Google Scholar

[4]

A. Ghose and Panos Ipeirotis. 2007. Designing Novel Review Ranking Systems: Predicting Usefulness and Impact of Reviews. ACM International Conference Proceeding Series 258. https://doi.org/10.1145/1282100.1282158

Digital Library

Google Scholar

[5]

Minqing Hu and Bing Liu. 2004. Mining and summarizing customer reviews. KDD-2004 - Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 168--177. https://doi.org/10.1145/1014052.1014073

Digital Library

Google Scholar

[6]

Minqing Hu and Bing Liu. 2004. Mining Opinion Features in Customer Reviews. Proceedings of AAAI.

Digital Library

Google Scholar

[7]

Fei Tony Liu, Kai Ting, and Zhi-Hua Zhou. 2009. Isolation Forest. 413--422. https://doi.org/10.1109/ICDM.2008.17

Digital Library

Google Scholar

Cited By

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Chen MMokdad LOthman JFourneau J(2022)LoRaLOFT-A Local Outlier Factor-based Malicious Nodes detection Method on MAC Layer for LoRaWANGLOBECOM 2022 - 2022 IEEE Global Communications Conference10.1109/GLOBECOM48099.2022.10000852(2026-2031)Online publication date: 4-Dec-2022
https://doi.org/10.1109/GLOBECOM48099.2022.10000852
Davis STabrizi N(2021)Customer Review Analysis: A Systematic Review2021 IEEE/ACIS 6th International Conference on Big Data, Cloud Computing, and Data Science (BCD)10.1109/BCD51206.2021.9581965(91-97)Online publication date: 13-Sep-2021
https://doi.org/10.1109/BCD51206.2021.9581965

Index Terms

Cerebro: Novelty Detection in Product Reviews
1. Theory of computation
  1. Theory and algorithms for application domains
    1. Machine learning theory
      1. Unsupervised learning and clustering

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Published In

ICMLSC '20: Proceedings of the 4th International Conference on Machine Learning and Soft Computing

January 2020

175 pages

ISBN:9781450376310

DOI:10.1145/3380688

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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NICT: National Institute of Information and Communications Technology

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 07 March 2020

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ICMLSC 2020

ICMLSC 2020: The 4th International Conference on Machine Learning and Soft Computing

January 17 - 19, 2020

Haiphong City, Viet Nam

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Cited By

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Chen MMokdad LOthman JFourneau J(2022)LoRaLOFT-A Local Outlier Factor-based Malicious Nodes detection Method on MAC Layer for LoRaWANGLOBECOM 2022 - 2022 IEEE Global Communications Conference10.1109/GLOBECOM48099.2022.10000852(2026-2031)Online publication date: 4-Dec-2022
https://doi.org/10.1109/GLOBECOM48099.2022.10000852
Davis STabrizi N(2021)Customer Review Analysis: A Systematic Review2021 IEEE/ACIS 6th International Conference on Big Data, Cloud Computing, and Data Science (BCD)10.1109/BCD51206.2021.9581965(91-97)Online publication date: 13-Sep-2021
https://doi.org/10.1109/BCD51206.2021.9581965

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Recommendations

Deep Learning for Anomaly Detection: A Review

Leveraging Anomaly Detection for Proactive Application Monitoring

A flexible framework for anomaly Detection via dimensionality reduction