Online product reviews are ubiquitous and helpful sources of information available to consumers for making purchase decisions. Consumers rely on both the quantitative aspects of reviews such as valence and volume as well as textual descriptions to learn about product quality and fit. In this paper we show how new achievements in natural language processing can provide an important assist for different kinds of review-related writing tasks. Working in the interesting context of wine reviews, we demonstrate that machines are capable of performing the critical marketing task of writing expert reviews directly from a fairly small amount of product attribute data (metadata). We conduct a kind of “Turing Test” to evaluate human response to our machine-written reviews and show strong support for the assertion that machines can write reviews that are indistinguishable from those written by experts. Rather than replacing the human review writer, we envision a workflow wherein machines take the metadata as inputs and generate a human readable review as a first draft of the review and thereby assist an expert reviewer in writing their review. We next modify and apply our machine-writing technology to show how machines can be used to write a synthesis of a set of product reviews. For this last application we work in the context of beer reviews (for which there is a large set of available reviews for each of a large number of products) and produce machine-written review syntheses that do a good job – measured again through human evaluation – of capturing the ideas expressed in the reviews of any given beer. For each of these applications, we adapt the Transformer neural net architecture. The work herein is broadly applicable in marketing, particularly in the context of online reviews. We close with suggestions for additional applications of our model and approach as well as other directions for future research.

在线产品评论是无处不在且有用的信息来源，可供消费者做出购买决定。消费者依靠评价的定量方面（例如价和量）以及文字描述来了解产品质量和合身性。在本文中，我们展示了自然语言处理方面的新成就如何为不同类型的与评论相关的写作任务提供重要帮助。在葡萄酒评论的有趣背景下工作，我们证明机器能够执行直接从相当少量的产品属性数据（元数据）撰写专家评论的关键营销任务。我们进行了一种“图灵测试”来评估人类对我们机器撰写的评论的反应，并强烈支持机器撰写的评论与专家撰写的评论无法区分的断言。我们没有取代人类评论作者，而是设想了一个工作流程，其中机器将元数据作为输入并生成人类可读的评论作为评论的初稿，从而协助专家评论者撰写他们的评论。我们接下来修改并应用我们的机器编写技术来展示如何使用机器来编写一组产品评论的综合。对于最后一个应用程序，我们在啤酒评论的背景下工作（对于大量产品中的每一个都有大量可用的评论）并生成做得很好的机器编写的评论综合 - 通过人工评估再次测量– 捕捉任何给定啤酒评论中表达的想法。对于这些应用中的每一个，我们都采用了 Transformer 神经网络架构。本文的工作广泛适用于市场营销，特别是在在线评论的背景下。最后，我们对我们的模型和方法的其他应用以及未来研究的其他方向提出了建议。特别是在在线评论的背景下。最后，我们对我们的模型和方法的其他应用以及未来研究的其他方向提出了建议。特别是在在线评论的背景下。最后，我们对我们的模型和方法的其他应用以及未来研究的其他方向提出了建议。