An Hybrid Clustering and BERT Model for Chinese Threads in Social Media
Yu-Hsuan Wu, Jheng-Long Wu · 2024
Communicating in online social media has become the main way of socializing today. Different from face-to-face conversations in the real world, the meaning of online conversations can only be interpreted through text, and it is difficult to observe the hidden meaning. In addition, users often use pronouns or omit subject words in online social media. In this way, the topic of the discussion thread may be led in a direction unrelated to the article content due to wrong interpretation. Most previous studies have explored the topics contained in long texts, such as the entire article content, but have not conducted research on topic clustering of discussion thread data. Therefore, this paper uses the data from social media platforms to identify five entity categories, such as people, events, times, locations, and things, and cluster the discussion thread data into the topic. Finally establish a Chinese discussion thread topic dataset. In addition, this paper uses two methods to obtain response representation and six existing topic clustering models to conduct preliminary topic hybrid clustering experiments on the Chinese discussion thread topic dataset.