survey

A Survey of Cutting-edge Multimodal Sentiment Analysis

Authors:

Kumar Abhishek,

Hiteshwar Kumar AzadAuthors Info & Claims

ACM Computing Surveys, Volume 56, Issue 9

Article No.: 227, Pages 1 - 38

https://doi.org/10.1145/3652149

Published: 25 April 2024 Publication History

Abstract

The rapid growth of the internet has reached the fourth generation, i.e., web 4.0, which supports Sentiment Analysis (SA) in many applications such as social media, marketing, risk management, healthcare, businesses, websites, data mining, e-learning, psychology, and many more. Sentiment analysis is a powerful tool for governments, businesses, and researchers to analyse users’ emotions and mental states in order to generate opinions and reviews about products, services, and daily activities. In the past years, several SA techniques based on Machine Learning (ML), Deep Learning (DL), and other soft computing approaches were proposed. However, growing data size, subjectivity, and diversity pose a significant challenge to enhancing the efficiency of existing techniques and incorporating current development trends, such as Multimodal Sentiment Analysis (MSA) and fusion techniques. With the aim of assisting the enthusiastic researcher to navigating the current trend, this article presents a comprehensive study of various literature to handle different aspects of SA, including current trends and techniques across multiple domains. In order to clarify the future prospects of MSA, this article also highlights open issues and research directions that lead to a number of unresolved challenges.

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Index Terms

A Survey of Cutting-edge Multimodal Sentiment Analysis
1. Computing methodologies
  1. Artificial intelligence
    1. Natural language processing
2. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Sentiment analysis

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Highlights
- We provide a new taxonomy of MABSA based on subtasks and data types.
- We innovatively divide MABSA datasets into aspect-based and category-based types.
- We conduct a systematic review of the latest MABSA Methods based on the ...
Subjectivity and sentiment analysis: An overview of the current state of the area and envisaged developments

In this introduction, we present an overview of the current state of research in the Natural Language Processing tasks of subjectivity and sentiment analysis, as well as their application domains and closely-related research field of emotion detection. ...
Multimodal Sentiment Analysis: A Survey of Methods, Trends, and Challenges
Sentiment analysis has come long way since it was introduced as a natural language processing task nearly 20 years ago. Sentiment analysis aims to extract the underlying attitudes and opinions toward an entity. It has become a powerful tool used by ...

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Published In

cover image ACM Computing Surveys

ACM Computing Surveys Volume 56, Issue 9

September 2024

980 pages

EISSN:1557-7341

DOI:10.1145/3613649

Editors:
David Atienza
Swiss Federal Institute of Technology Lausanne (EPFL), Switzerland
,
Michela Milano
University of Bologna, Italy

Issue’s Table of Contents

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 25 April 2024

Online AM: 11 March 2024

Accepted: 07 March 2024

Revised: 03 February 2024

Received: 21 April 2023

Published in CSUR Volume 56, Issue 9

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Shi XYang MHu MRen FKang XDing W(2025)Affective knowledge assisted bi-directional learning for Multi-modal Aspect-based Sentiment AnalysisComputer Speech & Language10.1016/j.csl.2024.10175591(101755)Online publication date: Apr-2025
https://doi.org/10.1016/j.csl.2024.101755
Yang ZHe QDu NHe Q(2025)Temporal text-guided feedback-based progressive fusion network for multimodal sentiment analysisAlexandria Engineering Journal10.1016/j.aej.2024.12.117116(699-709)Online publication date: Mar-2025
https://doi.org/10.1016/j.aej.2024.12.117
Li YLiu ALu Y(2025)Multi-level language interaction transformer for multimodal sentiment analysisJournal of Intelligent Information Systems10.1007/s10844-025-00923-xOnline publication date: 5-Feb-2025
https://doi.org/10.1007/s10844-025-00923-x
Alsekait DFathi HTaha MTaha ANabil ANawaz AAhmed ZAlshinwan MIssa MAbdElminaam D(2024)Empowering Retail Dual Transformer-Based Profound Product Recommendation Using Multi-Model ReviewJournal of Organizational and End User Computing10.4018/JOEUC.35800236:1(1-23)Online publication date: 7-Nov-2024
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Xu YZou PWang RLi QXu CZhao ZYang XSun XGuo DWang MAmiriparian SChrist LEulitz SKönig ACambria ESchuller B(2024)AMTN: Attention-Enhanced Multimodal Temporal Network for Humor DetectionProceedings of the 5th on Multimodal Sentiment Analysis Challenge and Workshop: Social Perception and Humor10.1145/3689062.3689375(65-69)Online publication date: 28-Oct-2024
https://dl.acm.org/doi/10.1145/3689062.3689375
Niu YXiang YLiu Y(2024)A Multimodal Unsupervised Clustering Model for Semantic Analysis Based on View Augmentation and Dynamic High-Quality Sample Selection2024 12th International Conference on Information Systems and Computing Technology (ISCTech)10.1109/ISCTech63666.2024.10845393(1-5)Online publication date: 8-Nov-2024
https://doi.org/10.1109/ISCTech63666.2024.10845393
Wang LLiu YWang H(2024)Research on Emotion Analysis of Multimodal Learning Supported by Deep Learning2024 IEEE 7th International Conference on Automation, Electronics and Electrical Engineering (AUTEEE)10.1109/AUTEEE62881.2024.10869743(476-480)Online publication date: 27-Dec-2024
https://doi.org/10.1109/AUTEEE62881.2024.10869743
Zhang LWei XYang FZhao CWen BLu Y(2024)Cross-Domain Sentiment Classification with Mere Contrastive Learning and Improved Method2024 3rd International Conference on Artificial Intelligence and Computer Information Technology (AICIT)10.1109/AICIT62434.2024.10730527(1-10)Online publication date: 20-Sep-2024
https://doi.org/10.1109/AICIT62434.2024.10730527
Liu Z(2024)The Construction of a Digital Dissemination Platform for the Intangible Cultural Heritage Using Convolutional Neural Network ModelsHeliyon10.1016/j.heliyon.2024.e40986(e40986)Online publication date: Dec-2024
https://doi.org/10.1016/j.heliyon.2024.e40986
Wang SRatnavelu KBin Shibghatullah A(2024)UEFN: Efficient uncertainty estimation fusion network for reliable multimodal sentiment analysisApplied Intelligence10.1007/s10489-024-06113-655:3Online publication date: 16-Dec-2024
https://dl.acm.org/doi/10.1007/s10489-024-06113-6
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