Targeted aspect based multimodal sentiment analysis:an attention capsule extraction and multi-head fusion network
This work addresses sentiment analysis for multimodal data with specific aspects, but it appears incremental as it builds on existing methods like MHA and ResNet.
The authors tackled targeted aspect-based multimodal sentiment analysis by proposing a new model that integrates attention capsule extraction and multi-head fusion, achieving effective results on two manually annotated datasets.
Multimodal sentiment analysis has currently identified its significance in a variety of domains. For the purpose of sentiment analysis, different aspects of distinguishing modalities, which correspond to one target, are processed and analyzed. In this work, we propose the targeted aspect-based multimodal sentiment analysis (TABMSA) for the first time. Furthermore, an attention capsule extraction and multi-head fusion network (EF-Net) on the task of TABMSA is devised. The multi-head attention (MHA) based network and the ResNet-152 are employed to deal with texts and images, respectively. The integration of MHA and capsule network aims to capture the interaction among the multimodal inputs. In addition to the targeted aspect, the information from the context and the image is also incorporated for sentiment delivered. We evaluate the proposed model on two manually annotated datasets. the experimental results demonstrate the effectiveness of our proposed model for this new task.