Utilizing review analysis to suggest product advertisement improvements

• Takaaki Tsunoda ^[1] ; Takashi Inui ^[1] ; Satoshi Sekine ^[2]
  1. [1] University of Tsukuba

     University of Tsukuba

     Japón

     
  2. [2] New York University

     New York University

     Estados Unidos

     
• Localización: 6th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis WASSA 2015: Workshop Proceedings : 17 September 2015 Lisboa, Portugal / Alexandra Balahur Dobrescu (ed. lit.), Erik van der Goot (ed. lit.), Piek Vossen (ed. lit.), Andrés Montoyo Guijarro (ed. lit.), 2015, ISBN 978-1-941643-32-7, págs. 41-50
• Idioma: inglés
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• Resumen

  • On an e-commerce site, product blurbs (short promotional statements) and user reviews give us a lot of information about products. While a blurb should be appealing to encourage more users to click on a product link, sometimes sellers may miss or misunderstand which aspects of the product are important to their users. We therefore propose a novel task: sug- gesting aspects of products for an advertisement improvement. As reviews have a lot of information about aspects from the perspective of users, review analysis enables us to suggest aspects that could attract more users. To achieve this, we break this task into the following two subtasks: aspect grouping and aspect group ranking. Aspect grouping enables us to treat product aspects at the semantic level rather than expression level. Aspect group ranking allows us to show users only aspects important for them. On the basis of experimental results using travel domain hotel data, we show that our proposed solution accomplishes NDCG@3 score of 0.739, which shows our solution is effective in achieving our goal.