社会化媒体
情绪分析
计算机科学
度量(数据仓库)
积极倾听
广告
数据科学
业务
万维网
数据挖掘
心理学
人工智能
沟通
作者
David A. Schweidel,Wendy W. Moe
摘要
In this research, the authors jointly model the sentiment expressed in social media posts and the venue format to which it was posted as two interrelated processes in an effort to provide a measure of underlying brand sentiment. Using social media data from firms in two distinct industries, they allow the content of the post and the underlying sentiment toward the brand to affect both processes. The results show that the inferences marketing researchers obtain from monitoring social media are dependent on where they “listen” and that common approaches that either focus on a single social media venue or ignore differences across venues in aggregated data can lead to misleading brand sentiment metrics. The authors validate the approach by comparing their model-based measure of brand sentiment with performance measures obtained from external data sets (stock prices for both brands and an offline brand-tracking study for one brand). They find that their measure of sentiment serves as a leading indicator of the changes observed in these external data sources and outperforms other social media metrics currently used.
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