  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.
