社交网络情感数据最为显著的特征是其动态性. 针对群体文本情感漂移分析任务, 提出一种高斯混合多层自编码器(GHVAE)用于情感漂移检测. GHVAE将高斯混合分布作为潜在分布的假设先验, 对应潜在分布的多中心性质从而提高模型性能. 此外, 还对原始HVAE模型内建的漂移度量算法进行改进, 改善了高漂移值之间过于接近导致分类性能下降的问题. 采用多项对照实验和消融实验用于验证GHVAE的性能, 实验结果显示新模型的创新点为其漂移检测表现带来了提升.

The most significant feature of social network sentiment data is its dynamic nature. Tackling public sentiment drift analysis, this study proposes a Gaussian mixture based hierarchical variational auto-encoder (GHVAE) model for detecting sentiment drifts. Specifically, the GHVAE applies Gaussian mixture distribution as the prior assumption of latent distribution, which corresponds to the multi-center of the latent distribution property to improve model performances. Moreover, the built-in drift measurement algorithm in the original HVAE model is revised to enlarge the distances among big drift scores and improve the classification performance. Several contrast and ablation experiments are implemented to validate the performance of the GHVAE. The results indicate the novelties of the GHVAE bring improvement in sentiment drift detection.

TP18

国家自然科学基金(62376143, 62106130, 62106130); 山西省基础研究计划青年科学研究项目(202203021212495, 20210302124084); 山西省高等学校科技创新项目(2021L283, 2021L284); 山西省基础研究计划(202303021211021); CCF-智谱AI大模型基金(CCF-Zhipu202310)