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Multimodal sentiment analysis: A systematic review of history, datasets, multimodal fusion methods, applications, challenges and future directions

Gandhi, Ankita; Adhvaryu, Kinjal; Poria, Soujanya; Cambria, Erik; Hussain, Amir


Ankita Gandhi

Kinjal Adhvaryu

Soujanya Poria

Erik Cambria


Sentiment analysis (SA) has gained much traction In the field of artificial intelligence (AI) and natural language processing (NLP). There is growing demand to automate analysis of user sentiment towards products or services. Opinions are increasingly being shared online in the form of videos rather than text alone. This has led to SA using multiple modalities, termed Multimodal Sentiment Analysis (MSA), becoming an important research area. MSA utilises latest advancements in machine learning and deep learning at various stages including for multimodal feature extraction and fusion and sentiment polarity detection, with aims to minimize error rate and improve performance. This survey paper examines primary taxonomy and newly released multimodal fusion architectures. Recent developments in MSA architectures are divided into ten categories, namely early fusion, late fusion, hybrid fusion, model-level fusion, tensor fusion, hierarchical fusion, bi-modal fusion, attention-based fusion, quantum-based fusion and word-level fusion. A comparison of several architectural evolutions in terms of MSA fusion categories and their relative strengths and limitations are presented. Finally, a number of interdisciplinary applications and future research directions are proposed.

Journal Article Type Article
Acceptance Date Sep 27, 2022
Online Publication Date Sep 28, 2022
Publication Date 2023-03
Deposit Date Jan 10, 2023
Journal Information Fusion
Print ISSN 1566-2535
Publisher Elsevier
Peer Reviewed Peer Reviewed
Volume 91
Pages 424-444
Keywords Affective computing, Sentiment analysis, Multimodal fusion, Fusion techniques