Refined Semantic Pattern Interaction Exploration for Multi-Attribute Documents: A Study on Subspace Topic Modeling and Visual Analysis

Existing approaches to semantic pattern exploration limit the comprehension of global semantic patterns, with few studies focusing on the fine-grained semantic pattern exploration that preserves contextual semantics. In this paper, this paper combine machine learning and visualization techniques in depth, employing a fine-grained semantic pattern visual analysis method for multi-attribute documents. This allows users to flexibly analyze fine-grained semantic patterns under different attributes while perceiving global semantic patterns. Firstly, this paper introduce a representation reconstruction network to obtain latent vectors that contain semantic and attribute information of multi-attribute documents, enabling the topic model to better identify subspace topics. Then, this paper introduce a human-in-the-loop semantic pattern visual analysis method and develop a semantic pattern visualization system that includes an exploration manager, a subspace projector, and a topic interpreter. This system supports users in selecting attribute subspaces for representation reconstruction, interactively exploring semantic patterns, and providing analysis results. Based on game review datasets, US news datasets, and Trump election datasets, this paper compare our proposed topic model with existing methods using topic diversity and topic coherence metrics. Experimental results show that the proposed topic model has good generalizability and flexibility in topic modeling. User experiments including semantic pattern exploration tasks verify the outstanding efficiency and effectiveness of the proposed method and visualization system in semantic pattern exploration.