This paper presents a domain-assisted approach to organize various aspects of a product into a hierarchy by integrating domain knowledge ( e.g. , the product specifications ) , as well as consumer reviews . Based on the derived hierarchy , we generate a hierarchical organization of consumer reviews on various product aspects and aggregate consumer opinions on these aspects . With such organization , user can easily grasp the overview of consumer reviews . Furthermore , we apply the hierarchy to the task of implicit aspect identification which aims to infer implicit aspects of the reviews that do not explicitly express those aspects but actually comment on them . The experimental results on 11 popular products in four domains demonstrate the effectiveness of our approach . With the rapidly expanding e-commerce , most retail Web sites encourage consumers to write reviews to express their opinions on various aspects of products . Huge collections of consumer reviews are now available on the Web . These reviews have become an important resource for both consumers and firms . Consumers commonly seek quality information from online consumer reviews prior to purchasing a product , while many firms use online reviews as an important resource in their product development , marketing , and consumer relationship management . However , the reviews are disorganized , leading to the difficulty in information navigation and knowledge acquisition . It is impractical for user to grasp the overview of consumer reviews and opinions on various aspects of a product from such enormous reviews . Among hundreds of product aspects , it is also inefficient for user to browse consumer reviews and opinions on a specific aspect . Thus , there is a compelling need to organize consumer reviews , so as to transform the reviews into a useful knowledge structure . Since the hierarchy can improve information representation and accessibility ( Cimiano , 2006 ) , we propose to organize the aspects of a product into a hierarchy and generate a hierarchical organization of consumer reviews accordingly . Towards automatically deriving an aspect hierarchy from the reviews , we could refer to traditional hierarchy generation methods in ontology learning , which first identify concepts from the text , then determine the parent-child relations between these concepts using either pattern-based or clusteringbased methods ( Murthy et al . , 2010 ) . However , pattern-based methods usually suffer from inconsistency of parent-child relationships among the concepts , while clustering-based methods often result in low accuracy . Thus , by directly utilizing these methods to generate an aspect hierarchy from consumer reviews , the resulting hierarchy is usually inaccurate , leading to unsatisfactory review organization . On the other hand , domain knowledge of products is now available on the Web . For example , there are more than 248,474 product specifications in the product selling Web site CNet.com ( Beckham , 2005 ) . These product specifications cover some product aspects and provide coarse-grained parentchild relations among these aspects . Such domain knowledge is useful to help organize the product as-140 pects into a hierarchy . However , the initial hierarchy obtained from domain knowledge usually can not fit the review data well . For example , the initial hierarchy is usually too coarse and may not cover the specific aspects commented in the reviews , while some aspects in the hierarchy may not be of interests to users in the reviews . Motivated by the above observations , we propose in this paper to organize the product aspects into a hierarchy by simultaneously exploiting the domain knowledge ( e.g. , the product specification ) and consumer reviews . With derived aspect hierarchy , we generate a hierarchical organization of consumer reviews on various aspects and aggregate consumer opinions on these aspects . Figure 1 illustrates a sample of hierarchical review organization for the product " iPhone 3 G " . With such organization , users can easily grasp the overview of product aspects as well as conveniently navigate the consumer reviews and opinions on any aspect . For example , users can find that 623 reviews , out of 9,245 reviews , are about the aspect " price " , with 241 positive and 382 negative reviews . Given a collection of consumer reviews on a specific product , we first automatically acquire an initial aspect hierarchy from domain knowledge and identify the aspects from the reviews . Based on the initial hierarchy , we develop a multi-criteria optimization approach to construct an aspect hierarchy to contain all the identified aspects . Our approach incrementally inserts the aspects into the initial hierarchy based on inter-aspect semantic distance , a metric used to measure the semantic relation among aspects . In order to derive reliable semantic distance , we propose to leverage external hierarchies , sampled from WordNet and Open Directory Project , to assist semantic distance learning . With resultant aspect hierarchy , the consumer reviews are then organized to their corresponding aspect nodes in the hierarchy . We then perform sentiment classification to determine consumer opinions on these aspects . Furthermore , we apply the hierarchy to the task of implicit aspect identification . This task aims to infer implicit aspects of the reviews that do not explicitly express those aspects but actually comment on them . For example , the implicit aspect of the review " It is so expensive " is " price . " Most existing aspect identification approaches rely on the appearance of aspect terms , and thus are not able to handle implicit aspect problem . Based on our aspect hierarchy , we can infer the implicit aspects by clustering the reviews into their corresponding aspect nodes in the hierarchy . We conduct experiments on 11 popular products in four domains . More details of the corpus are discussed in Section 4 . The experimental results demonstrate the effectiveness of our approach . The main contributions of this work can be summarized as follows : 1 ) We propose to hierarchically organize consumer reviews according to an aspect hierarchy , so as to transfer the reviews into a useful knowledge structure . 2 ) We develop a domain-assisted approach to generate an aspect hierarchy by integrating domain knowledge and consumer reviews . In order to derive reliable semantic distance between aspects , we propose to leverage external hierarchies to assist semantic distance learning . 3 ) We apply the aspect hierarchy to the task of implicit aspect identification , and achieve satisfactory performance . The rest of this paper is organized as follows . Our approach is elaborated in Section 2 and applied to implicit aspect identification in Section 3 . Section 4 presents the evaluations , while Section 5 reviews 141 related work . Finally , Section 6 concludes this paper with future works . In this paper , we have developed a domain-assisted approach to generate product aspect hierarchy by integrating domain knowledge and consumer reviews . Based on the derived hierarchy , we can generate a hierarchical organization of consumer reviews as well as consumer opinions on the aspects . With such organization , user can easily grasp the overview of consumer reviews , as well as seek consumer reviews and opinions on any specific aspect by navigating through the hierarchy . We have further applied the hierarchy to the task of implicit aspect identification . We have conducted evaluations on 11 different products in four domains . The experimental results have demonstrated the effectiveness of our approach . In the future , we will explore other linguistic features to learn the semantic distance between aspects , as well as apply our approach to other applications .

Challenge: Product review data is not structured hindering users from understanding its overview, and existing methods that build hierarchical structures suffer from inconsistency or low accuracy.
Approach: They propose to organize product aspects and formulize them into a hierarchical structure by exploiting existing domain knowledge-bases such as WordNet to navigate users.
Outcome: The proposed method that converts reviews into hierarchical structures exhibits effectiveness in experiments with 11 popular products from four domains.