The versatile nature of Visual Sentiment Analysis (VSA) is one reason for its rising profile. It isn't easy to efficiently manage social media data with visual information since previous research has concentrated on Sentiment Analysis (SA) of single modalities, like textual. In addition, most visual sentiment studies need to adequately classify sentiment because they are mainly focused on simply merging modal attributes without investigating their intricate relationships. This prompted the suggestion of developing a fusion of deep learning and machine learning algorithms. In this research, a deep feature-based method for multiclass classification has been used to extract deep features from modified ResNet50. Furthermore, gradient boosting algorithm has been used to classify photos containing emotional content. The approach is thoroughly evaluated on two benchmarked datasets, CrowdFlower and GAPED. Finally, cutting-edge deep learning and machine learning models were used to compare the proposed strategy. When compared to state-of-the-art approaches, the proposed method demonstrates exceptional performance on the datasets presented.

翻译：视觉情感分析（VSA）因其广泛的应用前景而日益受到关注。由于以往研究多集中于文本等单一模态的情感分析（SA），难以有效处理包含视觉信息的社交媒体数据。此外，多数视觉情感研究因仅简单融合模态特征而未深入探究其复杂关联，导致情感分类效果有限。为此，本研究提出融合深度学习与机器学习算法的解决方案。本工作采用基于深度特征的多类别分类方法，通过改进的ResNet50提取深度特征，并利用梯度提升算法对包含情感内容的图像进行分类。该方法在CrowdFlower和GAPED两个基准数据集上进行了全面评估，并与前沿的深度学习及机器学习模型进行了对比实验。结果表明，相较于现有先进方法，所提方案在目标数据集上展现出卓越的性能表现。