Main Article Content
In the Age of Multimedia and Social Networks, the proliferation of user-generated data has been nothing short of meteoric. This wealth of information necessitates careful analysis and processing to truly comprehend the subjective perceptions of users. At the intersection of this data-driven revolution, two critical fields emerge: Sentiment Analysis and Affective Computing. Sentiment analysis delves into the intricate realm of people's opinions, sentiments, evaluations, attitudes, and emotions as expressed through written language. On the other hand, Affective Computing focuses on the development of systems and devices capable of recognizing, interpreting, processing, and even simulating human emotions. It is, in essence, the fusion of Emotion AI and other affective technologies, with the overarching goal of enhancing people's lives.
With the exponential growth of user-generated data, thanks to social networks, wikis, and social tagging systems, it has become imperative to decipher the high-level semantics and user subjective perceptions embedded in this vast sea of information. Emotions and sentiments, in particular, stand out as significant facets of user-generated data, often carrying the emotional imprints of their creators.
The concurrent advancement of computational techniques for sentiment analysis and opinion mining has been accompanied by a surge in the utilization of psychological and cognitive models and theories. These are being harnessed to model sentiments and emotions, often in synergy with social computing techniques such as social network analysis and personalization, user review mining, and user profiling within social networks, among others. The synergy between affective/sentimental models and social computing techniques is not merely an academic endeavor; it paves the way for comprehending big data at a semantic level and enhances the performance of a wide array of social computing applications in this era of big data. This convergence not only combines affective and sentimental models with social computing but also charts a promising direction replete with opportunities for developing novel algorithms, methods, and tools.
It is a privilege for us to introduce the Special Issue on Sentiment Analysis and Affective computing in Multimedia Data on Social Network. Among the numerous research papers we received (50 in total), we meticulously selected 18 papers for publication. The overarching objective of this special issue is to delve into the recent advancements and disseminate state-of-the-art research related to sentiment analysis and affective computing in multimedia data within social networks and the technologies that make this possible. This special issue represents a showcase of new dimensions of research, offering researchers and industry professionals an illuminating perspective on sentiment analysis and affective computing in the realm of multimedia data within social networks.
We sincerely hope that the contributions in this special issue will not only inform but also inspire future research endeavors, leading to a deeper understanding of the multifaceted world of sentiment analysis and affective computing in the age of multimedia and social networks.