Abstract
Supplementing product information with user-generated content such as ratings and reviews can help to convert browsers into buyers. As a result this type of content is now front and centre for many major e-commerce sites such as Amazon. We believe that this type of content can provide a rich source of valuable information that is useful for a variety of purposes. In this work we are interested in harnessing past reviews to support the writing of new useful reviews, especially for novice contributors. We describe how automatic topic extraction and sentiment analysis can be used to mine valuable information from user-generated reviews, to make useful suggestions to users at review writing time about features that they may wish to cover in their own reviews. We describe the results of a live-user trial to show how the resulting system is capable of delivering high quality reviews that are comparable to the best that sites like Amazon have to offer in terms of information content and helpfulness.
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Dong, R., Schaal, M., O’Mahony, M.P., McCarthy, K., Smyth, B. (2013). Mining Features and Sentiment from Review Experiences. In: Delany, S.J., Ontañón, S. (eds) Case-Based Reasoning Research and Development. ICCBR 2013. Lecture Notes in Computer Science(), vol 7969. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-39056-2_5
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DOI: https://doi.org/10.1007/978-3-642-39056-2_5
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