Cited by 783Sentiment Analysis and Opinion MiningMultimodal Machine Learning ApplicationsEmotion and Mood Recognition

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

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Gandhi, Ankita, Adhvaryu, Kinjal, Poria, Soujanya, Cambria, Erik, Hussain, Amir·2023·Information Fusion

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Crossref · 10.1016/j.inffus.2022.09.025

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88 of 88 references verified0 citation errors · 0 not checkable

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  • Mid-term fusion typically employs attention mechanisms, feature weighting, connectivity, or GNNs to combine modality-specific features.

    “This method typically employs attention mechanisms, feature weighting, connectivity, or GNNs to combine modality specific features [41,42].”

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Authors
Gandhi, A., Adhvaryu, K., Poria, S., Cambria, E., & Hussain, A.
Affiliations
  • Gujarat Technological University (IN)
  • Singapore University of Technology and Design (SG)
  • Nanyang Technological University (SG)
  • Edinburgh Napier University (GB)
Corresponding author
Gandhi, Ankita
Venue
Information Fusion
Publisher
Elsevier BV
Published
2023-03
Volume
91
Pages
424-444
ISSN
1566-2535 (ISSN-L)
OpenAlex
W4297499129
Cited by
783
References
88
Citations by year
2026: 158 · 2025: 325 · 2024: 215 · 2023: 81 · 2022: 4
FWCI
11.00
Type
journal article
Language
en
Locations
  • Information Fusion (publishedVersion)
  • Edinburgh Napier Research Repository (Edinburgh Napier University) (acceptedVersion, other-oa)
  • DR-NTU (Nanyang Technological University) (submittedVersion)
Funders
  • Ministry of Education - Singapore (MOE-T2EP20220–0017)
  • Engineering and Physical Sciences Research Council (EP/M026981/1, EP/T021063/1, EP/T024917/1)
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › Sentiment Analysis and Opinion Mining
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Multimodal Machine Learning Applications
  • Social Sciences › Psychology › Experimental and Cognitive Psychology › Emotion and Mood Recognition
Keywords
multimodal sentiment analysis, multimodal fusion, feature-level fusion, decision fusion, hybrid fusion, attention fusion, tensor fusion, hierarchical fusion, polarity classification, multimodal feature extraction, video sentiment analysis, sentiment datasets

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