Year: 2025
Type: article
Abstract: With the increasing influence of social media in shaping consumer attitudes, modeling brand perception has become a critical task for computational marketing. Traditional approaches have predominantly relied on textual sentiment analysis, which fails to capture the dynamic interplay of user interaction and platform-specific affordances. To address this limitation, the authors propose a novel Multi-Dimensional Perception Modeling (MPM) framework that jointly incorporates content semantics, social engagement signals, and platform-level features into an end-to-end neural architecture. MPM leverages BERT-based encoders for text, structured encodings for likes, comments, and influencer metrics, and visibility-aware metadata to represent platform context. These heterogeneous inputs are fused using attention mechanisms to predict both brand attitude categories and perception scores. Experiments on large-scale datasets from Weibo and Xiaohongshu demonstrate the effectiveness of the model.
DOI: 10.4018/joeuc.388646
Authors: Yang Pu, Rui Ma, Jiachen Li, Yuanyuan Wei, Xingchen Pan, Xiaotian Zhang
Language: English
Subfield: Sociology and Political Science
Field: Social Sciences
Domain: Social Sciences
Open Access status: diamond
OpenAlex ID: W4414201107