Utilizing review analysis to suggest product advertisement improvements

Abstract

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: suggesting 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.