Product Feature Taxonomy Learning based on User Reviews

Nan Tian, Yue Xu, Yuefeng Li, Ahmad Abdel-Hafez, Audun Josang

2014

Abstract

In recent years, the Web 2.0 has provided considerable facilities for people to create, share and exchange information and ideas. Upon this, the user generated content, such as reviews, has exploded. Such data provide a rich source to exploit in order to identify the information associated with specific reviewed items. Opinion mining has been widely used to identify the significant features of items (e.g., cameras) based upon user reviews. Feature extraction is the most critical step to identify useful information from texts. Most existing approaches only find individual features about a product without revealing the structural relationships between the features which usually exist. In this paper, we propose an approach to extract features and feature relationships, represented as a tree structure called feature taxonomy, based on frequent patterns and associations between patterns derived from user reviews. The generated feature taxonomy profiles the product at multiple levels and provides more detailed information about the product. Our experiment results based on some popularly used review datasets show that our proposed approach is able to capture the product features and relations effectively.

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Paper Citation


in Harvard Style

Tian N., Xu Y., Li Y., Abdel-Hafez A. and Josang A. (2014). Product Feature Taxonomy Learning based on User Reviews . In Proceedings of the 10th International Conference on Web Information Systems and Technologies - Volume 2: WEBIST, ISBN 978-989-758-024-6, pages 184-192. DOI: 10.5220/0004850201840192


in Bibtex Style

@conference{webist14,
author={Nan Tian and Yue Xu and Yuefeng Li and Ahmad Abdel-Hafez and Audun Josang},
title={Product Feature Taxonomy Learning based on User Reviews},
booktitle={Proceedings of the 10th International Conference on Web Information Systems and Technologies - Volume 2: WEBIST,},
year={2014},
pages={184-192},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004850201840192},
isbn={978-989-758-024-6},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 10th International Conference on Web Information Systems and Technologies - Volume 2: WEBIST,
TI - Product Feature Taxonomy Learning based on User Reviews
SN - 978-989-758-024-6
AU - Tian N.
AU - Xu Y.
AU - Li Y.
AU - Abdel-Hafez A.
AU - Josang A.
PY - 2014
SP - 184
EP - 192
DO - 10.5220/0004850201840192