A Flexible Personalized Topic Query Scheme
Zhixing Lu, Zongmin Cui, Lihua Wang, Yang Xiao, Xiaolei Lv · 2020
Topic modeling is often used to visually analyze data in unstructured text corpora. However, in terms of the flexibility of topic modeling, the existing methods of topic modeling can only provide a single visual topic model, which cannot meet the needs of different users individually. Therefore, based on the existing research, this paper proposes a flexible personalized topic query scheme. Given a topic, the goal is to find multiple topics most relevant to the topic in a specific area. We use the real data to carry on the experiment, and the experiment shows that, compared with the existing relevant topic query scheme, the topic query scheme proposed in this paper is more advantageous.