Predicting emotions in online social networks: challenges and opportunities

Online social networking has become a popular means of information exchange and social interactions. Users of these platforms generate massive amounts of data about their relationships, behaviors, interests, opinions, locations visited, items purchased, and subjective experiences of various aspects of life. Moreover, these platforms enable people from wide-ranging social and cultural backgrounds to synergize and interact. One interesting area of research is the emotional dimensions contained in this user-generated content, specifically, emotion detection and prediction, which involve the extraction and analysis of emotions in social network data. This study aimed to provide a comprehensive overview and better understanding of the current state of research regarding emotion detection in online social networks by performing a systematic literature review (SLR). SLRs help identify the gaps, challenges, and opportunities in a field of study through a careful examination of current research to understand the methods and results, ultimately highlighting methodological concerns that can be used to improve future work in the field. Hence, we collected and analyzed studies that focused on emotion in social network posts and discussed various topics published in digital databases between 2010 and December 2020. Over 239 articles were initially included in the collection, and after the selection process and application of our quality criteria, 104 articles were examined, and the results showed a robust extant body of literature on the text-based emotion analysis model, while the image-based requires more attention as well as the multiple modality emotion analysis.