Kong Yeqiu, Xu Zhongwei, Mei Meng
School of Electronic and Information Engineering, Tongji University, Shanghai 201804, China.
Sensors (Basel). 2023 Aug 20;23(16):7282. doi: 10.3390/s23167282.
Social media is a real-time social sensor to sense and collect diverse information, which can be combined with sentiment analysis to help IoT sensors provide user-demanded favorable data in smart systems. In the case of insufficient data labels, cross-domain sentiment analysis aims to transfer knowledge from the source domain with rich labels to the target domain that lacks labels. Most domain adaptation sentiment analysis methods achieve transfer learning by reducing the domain differences between the source and target domains, but little attention is paid to the negative transfer problem caused by invalid source domains. To address these problems, this paper proposes a cross-domain sentiment analysis method based on feature projection and multi-source attention (FPMA), which not only alleviates the effect of negative transfer through a multi-source selection strategy but also improves the classification performance in terms of feature representation. Specifically, two feature extractors and a domain discriminator are employed to extract shared and private features through adversarial training. The extracted features are optimized by orthogonal projection to help train the classification in multi-source domains. Finally, each text in the target domain is fed into the trained module. The sentiment tendency is predicted in the weighted form of the attention mechanism based on the classification results from the multi-source domains. The experimental results on two commonly used datasets showed that FPMA outperformed baseline models.
社交媒体是一种实时社交传感器，用于感知和收集各种信息，它可以与情感分析相结合，以帮助物联网传感器在智能系统中提供用户所需的有利数据。在数据标签不足的情况下，跨域情感分析旨在将知识从标签丰富的源域转移到缺乏标签的目标域。大多数域适应情感分析方法通过减少源域和目标域之间的域差异来实现迁移学习，但很少关注无效源域导致的负迁移问题。为了解决这些问题，本文提出了一种基于特征投影和多源注意力的跨域情感分析方法（FPMA），该方法不仅通过多源选择策略减轻了负迁移的影响，还在特征表示方面提高了分类性能。具体来说，使用两个特征提取器和一个域判别器通过对抗训练来提取共享特征和私有特征。提取的特征通过正交投影进行优化，以帮助在多源域中训练分类。最后，将目标域中的每个文本输入到训练好的模块中。基于多源域的分类结果，以注意力机制的加权形式预测情感倾向。在两个常用数据集上的实验结果表明，FPMA的性能优于基线模型。
