Authors:
Nan Tian
1
;
Yue Xu
1
;
Yuefeng Li
1
;
Ahmad Abdel-Hafez
1
and
Audun Josang
2
Affiliations:
1
Queensland University of Technology, Australia
;
2
University of Oslo, Norway
Keyword(s):
Feature Extraction, Opinion Mining, Association Rules, Feature Taxonomy, User Reviews.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Data Mining
;
Databases and Information Systems Integration
;
Enterprise Information Systems
;
Sensor Networks
;
Signal Processing
;
Soft Computing
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 p
rovides 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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