E-commerce is growing at a swift pace, and the related content on the web is exploding. This is due to the shift of a massive amount of sales and bookings to the online platform. A large number of customers now prefer e-commerce to buy products or online booking for stays. After their transactions, customers post their experiences in the form of text reviews. Further, a new customer usually goes through these reviews before making an online transaction. However, many of such reviews include less important and often redundant information. This work aims to generate short pieces of useful text (‘tip’) from the large number of reviews which portray not only the relevant and unique information but also the sentiment captured from the reviews. The main motivation to generate a set of tips is to enable new customers to differentiate between competing for similar businesses. Our Tip Extraction Algorithm builds upon the existing methods by including the sentiments captured from the reviews. The proposed algorithms also emphasize the number of reviews for similarity comparison, so that proper weight could be given to amenities or other reviews‘ content. Recommender systems do not consider most of the recent businesses due to the vastness of the number of reviews of well established businesses. We compare our proposed method with the state-of-the-art TipSelector Algorithm for hotel tip extraction, on hotel reviews obtained from the TripAdvisor website. The proposed method works well, even when the number of available reviews is very less. Experimental results show significant improvements over the current state-of-the-art.

电子商务正以迅猛的速度增长，网络上的相关内容也在爆炸式增长。这是由于大量的销售和预订转移到了在线平台。现在，许多客户喜欢通过电子商务购买产品或在线预订住宿。交易之后，客户以文本评论的形式发布他们的经验。此外，新客户通常在进行在线交易之前要经过这些审查。但是，许多这样的评论都包括不太重要且通常是多余的信息。这项工作旨在从大量评论中生成有用的文字（“提示”），这些文字不仅描绘了相关且独特的信息，而且描绘了从评论中捕捉到的情感。产生一套技巧的主要动机是使新客户能够在竞争相似企业之间进行区分。我们的技巧提取算法通过包含从评论中捕获的观点来建立在现有方法的基础上。所提出的算法还强调了相似度比较的评论数量，因此可以对设施或其他评论的内容给予适当的权重。由于建立良好的业务的评论数量众多，因此推荐系统不会考虑大多数最新业务。在从TripAdvisor网站获得的酒店评论中，我们将我们提出的方法与最新的TipSelector算法进行了酒店提示提取。即使可用评论的数量非常少，所提出的方法也能很好地工作。