论文标题
具有预训练的语言模型的社交媒体中的时间感知主题识别：电动汽车的案例研究
Time-aware topic identification in social media with pre-trained language models: A case study of electric vehicles
论文作者
论文摘要
最近广泛的竞争性商业环境使公司始终关注社交媒体，因为人们对客户语言（例如需求，利益和投诉）的认可越来越多，这是未来机会的来源。该研究途径分析社交媒体数据在学术界受到了很多关注，但是它们的实用程序受到限制，因为大多数方法都提供了回顾性结果。此外，越来越多的客户生成的内容和迅速变化的主题使得有时间感知的主题演化分析的必要性。最近，一些研究人员将预训练的语言模型在社交媒体上的适用性作为输入功能，但在理解不断发展的主题方面留下了局限性。在这项研究中，我们建议采用预先训练的语言模型的时间感知主题识别方法。所提出的方法包括两个阶段：以语言模型跟踪时变主题的动力学功能和出现量表函数，以检查未来有希望的主题。在这里，我们将提出的方法应用于电动汽车的REDDIT数据，我们的发现突出了以时刻的方式从大量社交媒体中捕获新出现的客户主题的可行性。
Recent extensively competitive business environment makes companies to keep their eyes on social media, as there is a growing recognition over customer languages (e.g., needs, interests, and complaints) as source of future opportunities. This research avenue analysing social media data has received much attention in academia, but their utilities are limited as most of methods provide retrospective results. Moreover, the increasing number of customer-generated contents and rapidly varying topics have made the necessity of time-aware topic evolution analyses. Recently, several researchers have showed the applicability of pre-trained semantic language models to social media as an input feature, but leaving limitations in understanding evolving topics. In this study, we propose a time-aware topic identification approach with pre-trained language models. The proposed approach consists of two stages: the dynamics-focused function for tracking time-varying topics with language models and the emergence-scoring function to examine future promising topics. Here we apply the proposed approach to reddit data on electric vehicles, and our findings highlight the feasibility of capturing emerging customer topics from voluminous social media in a time-aware manner.
