Customer reviews represent a very rich data source from which we can extract very valuable information about different online shopping experiences. The amount of the collected data may be very large especially for trendy items (products, movies, TV shows, hotels, services...), where the number of available customers' opinions could easily surpass thousands. In fact, while a good number of reviews could indeed give a hint about the quality of an item, a potential customer may not have time or effort to read all reviews for the purpose of making an informed decision (buying, renting, booking...). Thus, the need for the right tools and technologies to help in such a task becomes a necessity for the buyer as for the seller. My research goal in this thesis is to develop reputation systems that can automatically provide E-commerce customers with valuable information to support them during their online decision-making process by mining online reviews expressed in natural language.
翻译：客户审查是一个非常丰富的数据来源,我们可以从中提取关于不同在线购物经验的非常有价值的信息。 所收集的数据数量可能非常大,特别是对于潮流物品(产品、电影、电视节目、酒店、服务...)而言(在这些物品上,现有客户的意见数量可能很容易超过数千个 ) 。 事实上,虽然大量审查确实可以提示某项目的质量,但潜在客户可能没有时间或精力阅读所有审查,以便作出知情决定(购买、租赁、预订 ) 。 因此,对买方来说,需要正确的工具和技术来帮助完成这一任务,就像对卖方一样。 我的论文研究目标是开发能自动为电子商务客户提供宝贵信息的名声系统,以便通过以自然语言进行的在线审查,在网上决策过程中支持他们。
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