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Combining similarity and sentiment in opinion mining for product recommendation

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Abstract

In the world of recommender systems, so-called content-based methods are an important approach that rely on the availability of detailed product or item descriptions to drive the recommendation process. For example, recommendations can be generated for a target user by selecting unseen products that are similar to the products that the target user has liked or purchased in the past. To do this, content-based methods must be able to compute the similarity between pairs of products (unseen products and liked products, for example) and typically this is achieved by comparing product features or other descriptive elements. The approach works well when product descriptions are readily available and when they are detailed enough to afford an effective similarity comparison. But this is not always the case. Detailed product descriptions may not be available since they can be expensive to create and maintain. In this article we consider another source of product descriptions in the form of the user-generated reviews that frequently accompany products on the web. We ask whether it is possible to mine these reviews, unstructured and noisy as they are, to produce useful product descriptions that can be used in a recommendation system. In particular we describe a novel approach to product recommendation that harnesses not only the features that can be mined from user-generated reviews but also the expressions of sentiment that are associated with these features. We present a recommendation ranking strategy that combines similarity and sentiment to suggest products that are similar but superior to a query product according to the opinion of reviewers, and we demonstrate the practical benefits of this approach across a variety of Amazon product domains.

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Notes

  1. The sentiment lexicon from Hu and Liu (2004a) contains lists of 2,009 positive words and 4,783 negative words. There are no weights assigned to words in the sentiment lexicon; rather, all words are considered to equally reflect either positive or negative sentiment.

  2. The range of both B1 and B2 is [-1, +1]. Since the range of S i m(Q,C) is [0, +1], S e n t(Q,C) is normalised to [0, +1] in (8).

  3. In this case, related products are those as suggested by Amazon’s “Customers who viewed this item also viewed these items” approach to recommendation.

  4. All terms in sentences are first converted to lowercase, stop words are removed and the remaining terms are stemmed to their root form.

  5. http://glaros.dtc.umn.edu/gkhome/views/cluto

  6. http://www.tweetfeel.com/

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Authors and Affiliations

  1. CLARITY: Centre for Sensor Web Technologies, School of Computer Science and Informatics, University College Dublin, Dublin, Ireland

    Ruihai Dong, Michael P. O’Mahony & Markus Schaal

  2. Insight Centre for Data Analytics, School of Computer Science and Informatics, University College Dublin, Dublin, Ireland

    Kevin McCarthy & Barry Smyth

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  1. Ruihai Dong
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Correspondence to Ruihai Dong.

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This work is supported by Science Foundation Ireland under grant 07/CE/I1147. The Insight Centre for Data Analytics is supported by Science Foundation Ireland under Grant Number SFI/12/RC/2289.

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Dong, R., O’Mahony, M.P., Schaal, M. et al. Combining similarity and sentiment in opinion mining for product recommendation. J Intell Inf Syst 46, 285–312 (2016). https://doi.org/10.1007/s10844-015-0379-y

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