Multimodal Semantics and Affective Computing from Multimedia Content

Advancements in technologies and increasing popularities of social media websites have enabled people to view, create, and share user-generated content (UGC) on the web. This results in a huge amount of UGC (e.g., photos, videos, and texts) on the web. Since such content depicts ideas, opinions, and interests of users, it requires analyzing the content efficiently to provide personalized services to users. Thus, it necessitates determining semantics and sentiments information from UGC. Such information help in decision making, learning, and recommendations. Since this chapter is based on the intuition that semantics and sentiment information are exhibited by different representations of data, the effectiveness of multimodal techniques is shown in semantics and affective computing. This chapter describes several significant multimedia analytics problems such as multimedia summarization, tag-relevance computation, multimedia recommendation, and facilitating e-learning and their solutions.