Xinjiang Multilingual Information Technology Laboratory, Xinjiang Multilingual Information Technology Research Center, College of Information Science and Engineering, Xinjiang University, Urumqi 830017, China.
Sensors (Basel). 2023 May 17;23(10):4829. doi: 10.3390/s23104829.
In sentiment analysis, biased user reviews can have a detrimental impact on a company's evaluation. Therefore, identifying such users can be highly beneficial as their reviews are not based on reality but on their characteristics rooted in their psychology. Furthermore, biased users may be seen as instigators of other prejudiced information on social media. Thus, proposing a method to help detect polarized opinions in product reviews would offer significant advantages. This paper proposes a new method for sentiment classification of multimodal data, which is called UsbVisdaNet (User Behavior Visual Distillation and Attention Network). The method aims to identify biased user reviews by analyzing their psychological behaviors. It can identify both positive and negative users and improves sentiment classification results that may be skewed due to subjective biases in user opinions by leveraging user behavior information. Through ablation and comparison experiments, the effectiveness of UsbVisdaNet is demonstrated, achieving superior sentiment classification performance on the Yelp multimodal dataset. Our research pioneers the integration of user behavior features, text features, and image features at multiple hierarchical levels within this domain.
在情感分析中，有偏见的用户评论可能会对公司的评价产生不利影响。因此，识别这些用户是非常有益的，因为他们的评论不是基于现实，而是基于他们根植于心理的特征。此外，有偏见的用户可能被视为社交媒体上其他有偏见信息的煽动者。因此，提出一种帮助检测产品评论中两极化观点的方法将具有重要优势。本文提出了一种新的多模态数据情感分类方法，称为 UsbVisdaNet（用户行为可视化提取和注意力网络）。该方法旨在通过分析他们的心理行为来识别有偏见的用户评论。它可以识别积极和消极的用户，并通过利用用户行为信息，改善可能因用户意见的主观偏见而产生偏差的情感分类结果。通过消融和对比实验，证明了 UsbVisdaNet 的有效性，在 Yelp 多模态数据集上实现了卓越的情感分类性能。我们的研究开创了在该领域内整合用户行为特征、文本特征和图像特征的先河。
