A Comprehensive Review of Visual-Textual Sentiment Analysis from Social Media Networks
Not on the map. Why?
Abstract (arXiv)
Social media networks have become a significant aspect of people's lives, serving as a platform for their ideas, opinions and emotions. Consequently, automated sentiment analysis (SA) is critical for recognising people's feelings in ways that other information sources cannot. The analysis of these feelings revealed various applications, including brand evaluations, YouTube film reviews and healthcare applications. As social media continues to develop, people post a massive amount of information in different forms, including text, photos, audio and video. Thus, traditional SA algorithms have become limited, as they do not consider the expressiveness of other modalities. By including such characteristics from various material sources, these multimodal data streams provide new opportunities for optimising the expected results beyond text-based SA. Our study focuses on the forefront field of multimodal SA, which examines visual and textual data posted on social media networks. Many people are more likely to utilise this information to express themselves on these platforms. To serve as a resource for academics in this rapidly growing field, we introduce a comprehensive overview of textual and visual SA, including data pre-processing, feature extraction techniques, sentiment benchmark datasets, and the efficacy of multiple classification methodologies suited to each field. We also provide a brief introduction of the most frequently utilised data fusion strategies and a summary of existing research on visual-textual SA. Finally, we highlight the most significant challenges and investigate several important sentiment applications.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 24 of its 217 references that are in the map. First posted on arXiv 5 Jul 2022.
Builds on
24 of 217 references.
References · 24
- 2015Deep Residual Learning for Image Recognitioncited by 51,809
- 2014Very Deep Convolutional Networks for Large-Scale Image Recognitioncited by 15,267
- 2015Rethinking the Inception Architecture for Computer Visioncited by 5,451
- 2013Efficient Estimation of Word Representations in Vector Spacecited by 4,346
- 2016Xception: Deep Learning with Depthwise Separable Convolutionscited by 2,320
- 2016Enriching Word Vectors with Subword Informationcited by 1,578
- 2017Tensor Fusion Network for Multimodal Sentiment Analysiscited by 429
- 2017Gated Multimodal Units for Information Fusioncited by 244
- 2018Deep Learning for Sentiment Analysis : A Surveycited by 139
- 2021Tensor Methods in Computer Vision and Deep Learningcited by 52
- 2021Bi-Bimodal Modality Fusion for Correlation-Controlled Multimodal Sentiment Analysiscited by 45
- 2016Detecting Sarcasm in Multimodal Social Platformscited by 43
12 more
- 2018Multimodal Sentiment Analysis with Word-Level Fusion and Reinforcement Learningcited by 43
- 2013NRC-Canada: Building the State-of-the-Art in Sentiment Analysis of Tweetscited by 33
- 2016The Evolution of Sentiment Analysis - A Review of Research Topics, Venues, and Top Cited Paperscited by 20
- 2016Sentiment Analysis of Twitter Data: A Survey of Techniquescited by 16
- 2017W2VLDA: Almost Unsupervised System for Aspect Based Sentiment Analysiscited by 12
- 2016Lexicon Integrated CNN Models with Attention for Sentiment Analysiscited by 8
- 2017Multimodal Classification for Analysing Social Mediacited by 8
- 2020A Deep Multi-Level Attentive network for Multimodal Sentiment Analysiscited by 6
- 2020BERTERS: Multimodal Representation Learning for Expert Recommendation System with Transformercited by 5
- 2019A Study of Feature Extraction techniques for Sentiment Analysiscited by 2
- 2017Joint Sentiment/Topic Modeling on Text Data Using Boosted Restricted Boltzmann Machinecited by 1
- 2021COVID-19 detection using deep convolutional neural networks and binary-differential-algorithm-based feature selection on X-ray imagescited by 1
Built on by
Citing papers per year: 2025: 1.
1 paper.