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

A Gandhi, K Adhvaryu, S Poria, E Cambria, A Hussain - Information Fusion, 2023 - Elsevier
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 …

Foundations & trends in multimodal machine learning: Principles, challenges, and open questions

PP Liang, A Zadeh, LP Morency - ACM Computing Surveys, 2024 - dl.acm.org
Multimodal machine learning is a vibrant multi-disciplinary research field that aims to design
computer agents with intelligent capabilities such as understanding, reasoning, and learning …

Multimodal sentiment analysis based on fusion methods: A survey

L Zhu, Z Zhu, C Zhang, Y Xu, X Kong - Information Fusion, 2023 - Elsevier
Sentiment analysis is an emerging technology that aims to explore people's attitudes toward
an entity. It can be applied in a variety of different fields and scenarios, such as product …

State of the art: a review of sentiment analysis based on sequential transfer learning

JYL Chan, KT Bea, SMH Leow, SW Phoong… - Artificial Intelligence …, 2023 - Springer
Recently, sequential transfer learning emerged as a modern technique for applying the
“pretrain then fine-tune” paradigm to leverage existing knowledge to improve the …

Multi-modal sarcasm detection via cross-modal graph convolutional network

B Liang, C Lou, X Li, M Yang, L Gui… - Proceedings of the …, 2022 - wrap.warwick.ac.uk
With the increasing popularity of posting multimodal messages online, many recent studies
have been carried out utilizing both textual and visual information for multi-modal sarcasm …

[HTML][HTML] Multibench: Multiscale benchmarks for multimodal representation learning

PP Liang, Y Lyu, X Fan, Z Wu, Y Cheng… - Advances in neural …, 2021 - ncbi.nlm.nih.gov
Learning multimodal representations involves integrating information from multiple
heterogeneous sources of data. It is a challenging yet crucial area with numerous real-world …

Beneath the tip of the iceberg: Current challenges and new directions in sentiment analysis research

S Poria, D Hazarika, N Majumder… - IEEE transactions on …, 2020 - ieeexplore.ieee.org
Sentiment analysis as a field has come a long way since it was first introduced as a task
nearly 20 years ago. It has widespread commercial applications in various domains like …

Multimodal sentimental analysis for social media applications: A comprehensive review

G Chandrasekaran, TN Nguyen… - … Reviews: Data Mining …, 2021 - Wiley Online Library
The analysis of sentiments is essential in identifying and classifying opinions regarding a
source material that is, a product or service. The analysis of these sentiments finds a variety …

Quantifying & modeling multimodal interactions: An information decomposition framework

PP Liang, Y Cheng, X Fan, CK Ling… - Advances in …, 2024 - proceedings.neurips.cc
The recent explosion of interest in multimodal applications has resulted in a wide selection
of datasets and methods for representing and integrating information from different …

Xmecap: Meme caption generation with sub-image adaptability

Y Chen, S Yan, Z Zhu, Z Li, Y Xiao - arXiv preprint arXiv:2407.17152, 2024 - arxiv.org
Humor, deeply rooted in societal meanings and cultural details, poses a unique challenge
for machines. While advances have been made in natural language processing, real-world …