Domain Independent Model for Product Attribute Extraction from User Reviews using Wikipedia

Proceedings of the 5th International Joint Conference on Natural Language Processing, pages 1408–1412, Chiang Mai, Thailand, November 8 – 13, 2011. c⃝2011 AFNLP Domain Independent Model for Product Attribute Extraction from User Reviews using Wikipedia Sudheer Kovelamudi+ Sethu Ramalingam∗ Arpit Sood+ Vasudeva Varma+ +International Institute of Information Technology, Hyderabad, India sudheer.k@research.iiit.ac.in, arpit.soodug08@students.iiit.ac.in, vv@iiit.ac.in ∗24/7 Customer Ilabs, India sethu.s@247customer.com Abstract The world of E-commerce is expanding, pos- ing a large arena of products, their descrip- tions, customer and professional reviews that are pertinent to them. Most of the prod- uct attribute extraction techniques in litera- ture work on structured descriptions using sev- eral text analysis tools. However, attributes in these descriptions are limited compared to those in customer reviews of a product, where users discuss deeper and more specific at- tributes. In this paper, we propose a novel su- pervised domain independent model for prod- uct attribute extraction from user reviews. The user generated content contains unstruc- tured and semi-structured text where conven- tional language grammar dependent tools like parts-of-speech taggers, named entity recog- nizers, parsers do not perform at expected lev- els. We used Wikipedia and Web to identify product attributes from customer reviews and achieved F1score of 0.73. 1 Introduction The online retail market is growing immense, offering millions of products for customers. The products are generally described in terms of a few set of attributes. Such product attributes are mined from the descrip- tions to represent the product in a structured manner. Often descriptions deal with generic attributes. For example, specific attributes like power consumption, pulsator, load, spin-dry effectiveness, noise, water us- age, water leakage, etc for a product like washing ma- chine cannot be correctly found in descriptions. On the other hand, customers express their opinions in the form of reviews. The opinions expressed are in terms of attributes they like and dislike but not always in terms of those attributes that are provided by the retailer for that particular product. Hence mining the attributes about which the customers discuss can be really helpful for retailers as well as for other customers. Mining product attributes from customer reviews can lead retailers to fetch and group other products that are having similar specific attributes and forecast more precisely. Hence many retailers are trying to en- rich their product knowledge bases with these domain specific and product specific attributes. Attribute ex- traction from reviews is also useful in tasks like review summarization, product rating, sales agent assessment, opinion mining of reviews, product recommendation systems, customer relationship management, customer satisfaction analysis, customer profiling, etc. On the customers’ side, they are prone to seek the opinions of other customers who actually used the product or bought it from a retailer website. They ask for unbiased evaluation of a product by leveraging information from multiple reviews, although each indi- vidual review can be subjective in nature. Therefore a person is more interested to read a featured review than overall reviews like “the product is really great, awesome!” or “this is the greatest …

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