Ontology induction for mining experiential knowledge from customer reviews

The rapid rate of product innovation, increasing degree of product complexity in categories ranging from digital cameras to toaster ovens fuels the need for resources to help consumers navigate the product space. For a given product category, rather than relying upon domain experts, we turn to the collective experience of the user community as captured in online customer reviews. In order to learn from users, we must first understand the concepts, the relationships, and the vocabulary with which the community discusses the product category. We induce the corresponding ontology from all reviews of all products in the category. In this paper, we represent the set of customer reviews for a product category as a graph. Vocabulary tokens are represented as vertices and edges capture the co-occurrence of tokens within and between reviews. We learn hyponym (is-a) relationships between concepts by identifying the connected components of the graph. Meronym/holonym (part-of/has-a) relationships are defined by heuristically searching for a maximal clique. We discuss preliminary results from approximately 8000 on-line digital camera reviews and specify a set of quantitative metrics that is currently being evaluated. Working Winter Information Systems Conference TLee 1 Paper Eccles School of Business Introduction At the intersection of current manufacturing trends such as mass customization, personalization, and short product life cycles, the modern consumer is faced with a dizzying selection of makes and models in a myriad of product categories. Customers formerly relied upon knowledgeable sales clerks and informed colleagues. Unfortunately, today's product variety challenges the ability of even professional reviewers like the Consumer's Union to keep pace. Consumer Reports might review 10 or 20 digital cameras in a single study. Amazon.com recently listed more than 600 available digital cameras (and camera packages). Fortunately, the World Wide Web has enabled a new knowledgebase for navigating the product space. On-line customer reviews represent the community of users. The challenge is to harness the knowledge contained within the virtual community of customer reviews to complement input from the physical community of colleagues, neighbors, and clerks. We previously proposed use-centric mining as one approach to leveraging reviews [18]. From the user community, as represented by customer reviews, we learn those features (product attributes) that align with particular product uses and applications. For example, what features does the user community make note of when purchasing a digital camera for vacation travel? Which features are noteworthy if 'taking eBay auction photos' is the primary reason for purchasing? A customer's prospective interests are matched to an associated set of product features. Products are recommended based upon their support for the desired mix of features. In this position paper, we present a graph-based approach to semi-automatically learn a domain-specific ontology of product features for the purpose of enabling use-centric mining. We define an ontology as a controlled vocabulary for discussing both concepts and relationships between concepts [13, 21]. We learn a category-specific ontology of product features from the Working Winter Information Systems Conference TLee 2 Paper Eccles School of Business customer reviews of products in that category. For example, the 'battery types' used for motor vehicles are not the same 'battery types' used in digital cameras. Using a common vocabulary of product features (concepts) enables use-centric mining to discover associations between user interests and product features from customer reviews. We model the space of online customer reviews in a product category as a weighted graph where vocabulary terms are vertices in the graph and edges are weighted by the frequency with which two terms are used together to describe a product feature. Three contributions that follow from our graph-based approach are presented in this position paper. First, we use cooccurrences of terms within and between review documents to heuristically prune the graph. Second, the principle of connected components is used to automatically define subclass or hyponym (is-a) relationships between concepts. Third, we use cliques to automatically discover meronym/holonym (part-of/has-a) relationships between concepts. In the remainder of this paper, we begin with a background statement to both motivate our work and to clarify the problem statement. We then provide a formal model and include initial results from processing a set of 8695 digital camera reviews from Epinions.com [1]. The preliminary status of this research leads us to describe a more formal set of evaluation methods that we are in the process of conducting but for which we do not yet have results. We close with a review of related work on ontology learning, a discussion, and future work.