Moving From Narrative to Interactive Multi-Modal Sentiment Analysis: A Survey
Junxia Ma, Lu Rong, Yazhou Zhang, Prayag Tiwari
Abstract
A growing number of individuals are expressing their opinions and engaging in interactive communication with others through various modalities, including natural language (text), facial gestures (vision), acoustic behaviors (audio), and more. Within the realms of natural language processing (NLP) and artificial intelligence (AI), multi-modal sentiment analysis has consistently remained a fundamental research area. Building upon recent advancements, this survey aims to provide researchers with a comprehensive overview of the state-of-the-art techniques in multi-modal sentiment analysis, specifically focusing on various sentiment interaction tasks. It is worth noting that the existing literature on multi-modal sentiment analysis has rarely delved into the realm of sentiment interaction. This survey presents a novel perspective by outlining the progression of multi-modal sentiment analysis from narrative sentiment to interactive sentiment. Furthermore, it discusses the research background, problem definition, and various approaches in multi-modal sentiment analysis. Additionally, this survey provides insights into the development of multi-modal sarcasm recognition, emphasizing the shift from narrativity to interactivity. Lastly, we summarize the current scientific challenges related to interaction modeling and highlight future development trends in the field.