The amount of digital text-based consumer review data has increased dramatically and there exist many machine learning approaches for automated text-based sentiment analysis. Marketing researchers have employed various methods for analyzing text reviews but lack a comprehensive comparison of their performance to guide method selection in future applications. We focus on the fundamental relationship between a consumer’s overall empirical evaluation, and the text-based explanation of their evaluation. We study the empirical tradeoff between predictive and diagnostic abilities, in applying various methods to estimate this fundamental relationship. We incorporate methods previously employed in the marketing literature, and methods that are so far less common in the marketing literature. For generalizability, we analyze 25,241 products in nine product categories, and 260,489 reviews across five review platforms. We find that neural network-based machine learning methods, in particular pre-trained versions, offer the most accurate predictions, while topic models such as Latent Dirichlet Allocation offer deeper diagnostics. However, neural network models are not suited for diagnostic purposes and topic models are ill equipped for making predictions. Consequently, future selection of methods to process text reviews is likely to be based on analysts’ goals of prediction versus diagnostics.

基于数字文本的消费者评论数据的数量急剧增加，并且存在许多用于基于文本的自动情感分析的机器学习方法。营销研究人员采用了各种方法来分析文本评论，但缺乏对其性能的全面比较来指导未来应用中的方法选择。我们关注消费者的整体经验评价与基于文本的评价解释之间的基本关系。我们研究了预测能力和诊断能力之间的经验权衡，并应用各种方法来估计这种基本关系。我们结合了以前在营销文献中使用的方法，以及迄今为止在营销文献中不太常见的方法。为了概括性，我们分析 25，九个产品类别的 241 种产品，以及来自五个评论平台的 260,489 条评论。我们发现基于神经网络的机器学习方法，特别是预训练版本，提供最准确的预测，而诸如潜在狄利克雷分配等主题模型提供更深入的诊断。然而，神经网络模型不适合诊断目的，主题模型也不适合进行预测。因此，未来处理文本评论的方法的选择很可能基于分析师的预测与诊断目标。神经网络模型不适合诊断目的，主题模型不适合进行预测。因此，未来处理文本评论的方法的选择很可能基于分析师的预测与诊断目标。神经网络模型不适合诊断目的，主题模型不适合进行预测。因此，未来处理文本评论的方法的选择很可能基于分析师的预测与诊断目标。