Tools for analyzing social media text data to gain marketing insight have recently emerged. While a wealth of research has focused on automated human personality assessment, little research has focused on advancing methods for obtaining brand personality from social media content. Brand personality is a nuanced aspect of brands that has a consistent set of traits aside from its functional benefits. In this study, we introduce a novel, automated, and generalizable data analytics approach to extract near real-time estimates of brand personalities in social media networks. This method can be used to track attempts to change brand personality over time, measure brand personality of competitors, and assess congruence in brand personality. Applied to consumer data, firms can assess how consumers perceive brand personality and study the effects of brand–consumer congruence in personality. Our approach develops a novel hybrid machine learning algorithmic design (LDA2Vec), which bypasses often extensive manual coding tasks, thus providing an adaptable and scalable tool that can be used for a range of management studies. Our approach enhances the theoretical understanding of channeled and perceived brand personality as it is represented in social media networks and provides practitioners with the ability to foster branding strategies by using big data resources.

最近出现了用于分析社交媒体文本数据以获得营销洞察力的工具。虽然大量研究都集中在自动化的人类个性评估上，但很少有研究关注推进从社交媒体内容中获取品牌个性的方法。品牌个性是品牌的一个微妙方面，除了其功能优势外，还具有一组一致的特征。在这项研究中，我们引入了一种新颖的、自动化的、可推广的数据分析方法来提取社交媒体网络中品牌个性的近实​​时估计。此方法可用于跟踪随时间改变品牌个性的尝试、衡量竞争对手的品牌个性以及评估品牌个性的一致性。应用于消费者数据，公司可以评估消费者如何看待品牌个性并研究品牌-消费者一致性对个性的影响。我们的方法开发了一种新颖的混合机器学习算法设计 (LDA2Vec)，它绕过了通常大量的手动编码任务，从而提供了一种适应性强且可扩展的工具，可用于一系列管理研究。我们的方法增强了对社交媒体网络中所代表的渠道和感知品牌个性的理论理解，并为从业者提供了使用大数据资源制定品牌战略的能力。因此提供了一种适应性强且可扩展的工具，可用于一系列管理研究。我们的方法增强了对社交媒体网络中所代表的渠道和感知品牌个性的理论理解，并为从业者提供了使用大数据资源制定品牌战略的能力。因此提供了一种适应性强且可扩展的工具，可用于一系列管理研究。我们的方法增强了对社交媒体网络中所代表的渠道和感知品牌个性的理论理解，并为从业者提供了使用大数据资源制定品牌战略的能力。