This paper develops a generalizable, machine-learning-based method for measuring established marketing constructs using passive analysis of consumer-generated textual data from service reviews. The method is demonstrated using topic and sentiment analysis along dimensions of an existing scale: lodging quality index (LQI).,The method induces numerical scale ratings from text-based data such as consumer reviews. This is accomplished by automatically developing a dictionary from words within a set of existing scale items, rather a more manual process. This dictionary is used to analyze textual consumer review data, inducing topic and sentiment along various dimensions. Data produced is equivalent with Likert scores.,Paired t-tests reveal that the text analysis technique the authors develop produces data that is equivalent to Likert data from the same individual. Results from the authors’ second study apply the method to real-world consumer hotel reviews.,Results demonstrate a novel means of using natural language processing in a way to complement or replace traditional survey methods. The approach the authors outline unlocks the ability to rapidly and efficiently analyze text in terms of any existing scale without the need to first manually develop a dictionary.,The technique makes a methodological contribution by outlining a new means of generating scale-equivalent data from text alone. The method has the potential to both unlock entirely new sources of data and potentially change how service satisfaction is assessed and opens the door for analysis of text in terms of a wider range of constructs.

中文翻译：

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从基于文本的服务评论中估算数字量表等级

本文开发了一种基于机器学习的通用性方法，该方法通过使用对来自服务评论的消费者生成的文本数据进行被动分析来衡量已建立的营销结构。使用主题和情感分析以及现有量表的维度（住宿质量指数（LQI））演示了该方法。该方法从基于文本的数据（如消费者评论）中得出数字量表评分。这是通过根据一组现有比例尺项目中的单词自动开发字典来完成的，而不是手动操作。该词典用于分析消费者的文本评论数据，从各个维度引发话题和情感。产生的数据与李克特分数相当。配对的t检验表明，作者开发的文本分析技术产生的数据等效于来自同一个人的Likert数据。作者第二项研究的结果将该方法应用于现实世界中的消费者酒店评论。结果证明了一种使用自然语言处理以补充或替代传统调查方法的新颖方法。作者概述的方法无需首先手动开发字典即可释放以现有规模快速有效地分析文本的能力。该技术通过概述一种从文本生成规模等效数据的新方法，为方法学做出了贡献单独。