School of Business, Xinyang Normal University, Xinyang, Henan, 464000, China.
BMC Psychol. 2024 Nov 21;12(1):681. doi: 10.1186/s40359-024-02186-7.
With the widespread proliferation of the Internet, social networking sites have increasingly become integrated into the daily lives of university students, leading to a growing reliance on these platforms. Several studies have suggested that this emotional dependence on social networking sites stems from unmet psychological needs. Meanwhile, social rejection has been identified as a prevalent phenomenon that exacerbates the deficiency of individual psychological needs. However, existing research on aspect-level sentiment analysis among college students within social networking sites faces challenges such as inadequate feature extraction, ineffective handling of data noise, and the neglect of complex interactions in multimodal data. To address these issues, this paper introduces a novel approach, the Multi-Granular View Dynamic Fusion Model (MVDFM), developed from both coarse-grained and fine-grained perspectives. MVDFM extracts multi-granular view features from textual and visual content, incorporating a dynamic gating self-attention mechanism. Additionally, it proposes a three-view decomposition higher-order pooling mechanism for a two-stage dynamic fusion of these features. Experimental results demonstrate the model's effectiveness, achieving accuracy and F1 values of 78.78% and 74.48% on the Twitter-2015 dataset, and 73.89% and 72.47% on the Twitter-2017 dataset, respectively. This efficient supervision enables the extraction of deep semantic information from multimodal data generated by college students on social networking sites. The model adeptly mines pertinent information related to target aspect-based words, enhancing the efficacy of aspect-level emotion prediction. Furthermore, it facilitates an effective exploration of the intricate interplay between social rejection, monitoring on social networking sites, the fear of missing out, and dependence on social networking sites, ultimately aiding university students in regulating their emotional management.
随着互联网的广泛普及，社交网站越来越融入大学生的日常生活，导致他们越来越依赖这些平台。有几项研究表明，这种对社交网站的情感依赖源于未满足的心理需求。同时，社交排斥已被确定为一种普遍现象，加剧了个体心理需求的不足。然而，现有的社交网站中大学生方面级情感分析研究面临着一些挑战，如特征提取不足、数据噪声处理效果不佳以及忽略多模态数据中的复杂交互。针对这些问题，本文提出了一种新颖的方法，即多粒度视图动态融合模型（MVDFM），它从粗粒度和细粒度两个方面进行了研究。MVDFM 从文本和视觉内容中提取多粒度视图特征，引入动态门控自注意力机制。此外，它还提出了一种三视图分解高阶池化机制，用于这两个阶段的特征动态融合。实验结果表明，该模型在 Twitter-2015 数据集上的准确率和 F1 值分别达到了 78.78%和 74.48%，在 Twitter-2017 数据集上的准确率和 F1 值分别达到了 73.89%和 72.47%，具有较好的效果。这种高效的监督方式可以从社交网站上大学生生成的多模态数据中提取出深层语义信息。该模型能够很好地挖掘与目标方面相关的词的相关信息，提高方面级情感预测的效果。此外，它还可以有效地探索社交排斥、社交网站监控、错失恐惧和社交网站依赖之间的复杂相互作用，最终帮助大学生调节他们的情绪管理。
