Multimodal sentiment analysis for social media contents during public emergencies

Nowadays, public opinions during public emergencies involve not only textual contents but also contain images. However, the existing works mainly focus on textual contents and they do not provide a satisfactory accuracy of sentiment analysis, lacking the combination of multimodal contents. In this paper, we propose to combine texts and images generated in the social media to perform sentiment analysis.