Multimodal sentiment analysis has attracted increasing attention with broad application prospects. The existing methods focuses on single modality, which fails to capture the social media content for multiple modalities. Moreover, in multi-modal learning, most of the works have focused on simply combining the two modalities, without exploring the complicated correlations between them. This resulted in dissatisfying performance for multimodal sentiment classification. Motivated by the status quo, we propose a Deep Multi-Level Attentive network, which exploits the correlation between image and text modalities to improve multimodal learning. Specifically, we generate the bi-attentive visual map along the spatial and channel dimensions to magnify CNNs representation power. Then we model the correlation between the image regions and semantics of the word by extracting the textual features related to the bi-attentive visual features by applying semantic attention. Finally, self-attention is employed to automatically fetch the sentiment-rich multimodal features for the classification. We conduct extensive evaluations on four real-world datasets, namely, MVSA-Single, MVSA-Multiple, Flickr, and Getty Images, which verifies the superiority of our method.
Multimodal Aspect-Level Sentiment Analysis Based on Gated Noise Filtering and Emotion-Relevance Interaction
Hubei University（湖北大学）；Key Laboratory of Intelligent Sensing System and Security (Hubei University), Ministry of Education（教育部智能感知系统与安全重点实验室（湖北大学））；Hubei Key Laboratory of Big Data Intelligent Analysis and Application (Hubei University)（湖北省大数据智能分析与应用重点实验室（湖北大学））
TF-Mamba: Text-enhanced Fusion Mamba with Missing Modalities for Robust Multimodal Sentiment Analysis
School of Cyber Science and Technology, Beihang University（北京航空航天大学网络空间安全学院）；School of Computer Science and Engineering, Beihang University（北京航空航天大学计算机科学与工程学院）
Mixture of Disentangled Experts with Missing Modalities for Robust Multimodal Sentiment Analysis
School of Cyber Science and Technology, Beihang University（北京航空航天大学网络空间安全学院）；School of Computer Science and Engineering, Beihang University（北京航空航天大学计算机学院）；School of Information Science and Technology, Beijing University of Technology（北京工业大学信息学部）
School of Computer Science and Engineering, NTU（南洋理工大学计算机科学与工程学院）；School of Natural Sciences, University of Stirling（斯特灵大学自然科学学院）；Temasek Laboratories, NTU（南洋理工大学淡马锡实验室）；National Institute of Technology, Warangal（瓦朗加尔国立理工学院）
M-SENA: An Integrated Platform for Multimodal Sentiment Analysis
Tsinghua University（清华大学）；Beijing National Research Center for Information Science and Technology（北京信息科学与技术国家研究中心）；Hebei University of Science and Technology（河北科技大学）
Synthesizing Sentiment-Controlled Feedback For Multimodal Text and Image Data
Center for Machine Vision and Signal Analysis, University of Oulu（机器视觉与信号分析中心，奥卢大学）；Indian Institute of Technology Roorkee（印度理工学院罗尔基分校）；State Key Laboratory of Blockchain and Data Security, Zhejiang University（区块链与数据安全国家重点实验室，浙江大学）
Divide and Conquer: Mixture-of-Bottleneck Experts in Informative Ordinal Space for Video-based Multimodal Sentiment Analysis
College of Computer Science and Software Engineering, Shenzhen University（深圳大学计算机科学与软件工程学院）；School of Electronics and Information Technology, Sun Yat-sen University（中山大学电子信息工程学院）；School of Cyberspace Security, Guangzhou University（广州大学网络空间安全学院）；School of Computer Science, South China Normal University（华南师范大学计算机学院）；VinUniversity（越南河内VinUniversity）；Nanyang Technological University（南洋理工大学）；School of Electrical and Electronic Engineering, Nanyang Technological University（南洋理工大学电气与电子工程学院）；Desay SV Automotive Co., Ltd（德赛西威汽车电子股份有限公司）
PTA: Enhancing Multimodal Sentiment Analysis through Pipelined Prediction and Translation-based Alignment
National University of Defense Technology（国防科技大学）；Hefei University of Technology（合肥工业大学）；Central China Normal University（华中师范大学）；Anhui Agricultural University（安徽农业大学）
Learning Language-guided Adaptive Hyper-modality Representation for Multimodal Sentiment Analysis
The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳））；China University of Geosciences, Wuhan（中国地质大学（武汉））；Nanyang Technological University（南洋理工大学）；Shenzhen Institute of Artificial Intelligence and Robotics for Society（深圳人工智能与机器人研究院）
Towards Robust Multimodal Sentiment Analysis with Incomplete Data
School of Data Science, The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳）数据科学学院）；University College London（伦敦大学学院）；School of Computer Science, Wuhan University（武汉大学计算机学院）
Exploiting Diverse Feature for Multimodal Sentiment Analysis
Hefei University of Technology（合肥工业大学）；Anhui University（安徽大学）；Hefei University of Technology Institute of Artificial Intelligence, Hefei Comprehensive National Science Center（合肥工业大学人工智能研究所、合肥综合性国家科学中心）
