# A Comprehensive Review of Visual-Textual Sentiment Analysis from Social Media Networks

arXiv: https://arxiv.org/abs/2207.02160 · HTML: https://mlsanity.com/paper/2207.02160/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 217 references that are in the map.
- First posted on arXiv 5 Jul 2022.

## Builds on (most cited first, 24 of 24)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Rethinking the Inception Architecture for Computer Vision](https://mlsanity.com/paper/1512.00567/) (2015)
- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [Xception: Deep Learning with Depthwise Separable Convolutions](https://mlsanity.com/paper/1610.02357/) (2016)
- [Enriching Word Vectors with Subword Information](https://mlsanity.com/paper/1607.04606/) (2016)
- [Tensor Fusion Network for Multimodal Sentiment Analysis](https://mlsanity.com/paper/1707.07250/) (2017)
- [Gated Multimodal Units for Information Fusion](https://mlsanity.com/paper/1702.01992/) (2017)
- [Deep Learning for Sentiment Analysis : A Survey](https://mlsanity.com/paper/1801.07883/) (2018)
- [Tensor Methods in Computer Vision and Deep Learning](https://mlsanity.com/paper/2107.03436/) (2021)
- [Bi-Bimodal Modality Fusion for Correlation-Controlled Multimodal Sentiment Analysis](https://mlsanity.com/paper/2107.13669/) (2021)
- [Detecting Sarcasm in Multimodal Social Platforms](https://mlsanity.com/paper/1608.02289/) (2016)
- [Multimodal Sentiment Analysis with Word-Level Fusion and Reinforcement Learning](https://mlsanity.com/paper/1802.00924/) (2018)
- [NRC-Canada: Building the State-of-the-Art in Sentiment Analysis of Tweets](https://mlsanity.com/paper/1308.6242/) (2013)
- [The Evolution of Sentiment Analysis - A Review of Research Topics, Venues, and Top Cited Papers](https://mlsanity.com/paper/1612.01556/) (2016)
- [Sentiment Analysis of Twitter Data: A Survey of Techniques](https://mlsanity.com/paper/1601.06971/) (2016)
- [W2VLDA: Almost Unsupervised System for Aspect Based Sentiment Analysis](https://mlsanity.com/paper/1705.07687/) (2017)
- [Lexicon Integrated CNN Models with Attention for Sentiment Analysis](https://mlsanity.com/paper/1610.06272/) (2016)
- [Multimodal Classification for Analysing Social Media](https://mlsanity.com/paper/1708.02099/) (2017)
- [A Deep Multi-Level Attentive network for Multimodal Sentiment Analysis](https://mlsanity.com/paper/2012.08256/) (2020)
- [BERTERS: Multimodal Representation Learning for Expert Recommendation System with Transformer](https://mlsanity.com/paper/2007.07229/) (2020)
- [A Study of Feature Extraction techniques for Sentiment Analysis](https://mlsanity.com/paper/1906.01573/) (2019)
- [Joint Sentiment/Topic Modeling on Text Data Using Boosted Restricted Boltzmann Machine](https://mlsanity.com/paper/1711.03736/) (2017)
- [COVID-19 detection using deep convolutional neural networks and binary-differential-algorithm-based feature selection on X-ray images](https://mlsanity.com/paper/2104.07279/) (2021)

## Built on by (most cited first, 1 of 1)

- [SentiFormer: Metadata Enhanced Transformer for Image Sentiment Analysis](https://mlsanity.com/paper/2502.15322/) (2025)

## 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.