Time-aware topic identification in social media with pre-trained language models: A case study of electric vehicles
This work addresses the need for time-aware topic evolution analysis in social media for companies to monitor customer languages, but it is incremental as it builds on existing methods with language models.
The study tackled the problem of identifying evolving topics in social media by proposing a time-aware approach using pre-trained language models, applied to Reddit data on electric vehicles to capture emerging customer topics from voluminous data.
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.