# A Fair and Comprehensive Comparison of Multimodal Tweet Sentiment Analysis Methods

arXiv: https://arxiv.org/abs/2106.08829 · HTML: https://mlsanity.com/paper/2106.08829/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 49 references that are in the map.
- First posted on arXiv 16 Jun 2021.

## Builds on (most cited first, 18 of 18)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [ImageNet Large Scale Visual Recognition Challenge](https://mlsanity.com/paper/1409.0575/) (2014)
- [ViLBERT: Pretraining Task-Agnostic Visiolinguistic Representations for Vision-and-Language Tasks](https://mlsanity.com/paper/1908.02265/) (2019)
- [Oscar: Object-Semantics Aligned Pre-training for Vision-Language Tasks](https://mlsanity.com/paper/2004.06165/) (2020)
- [Convolutional Neural Networks for Sentence Classification](https://mlsanity.com/paper/1408.5882/) (2014)
- [Challenges in Representation Learning: A report on three machine learning contests](https://mlsanity.com/paper/1307.0414/) (2013)
- [DSFD: Dual Shot Face Detector](https://mlsanity.com/paper/1810.10220/) (2018)
- [Building a Large Scale Dataset for Image Emotion Recognition: The Fine Print and The Benchmark](https://mlsanity.com/paper/1605.02677/) (2016)
- [Robust Image Sentiment Analysis Using Progressively Trained and Domain Transferred Deep Networks](https://mlsanity.com/paper/1509.06041/) (2015)
- [PDANet: Polarity-consistent Deep Attention Network for Fine-grained Visual Emotion Regression](https://mlsanity.com/paper/1909.05693/) (2019)
- [Lexicon Integrated CNN Models with Attention for Sentiment Analysis](https://mlsanity.com/paper/1610.06272/) (2016)
- [A Combined CNN and LSTM Model for Arabic Sentiment Analysis](https://mlsanity.com/paper/1807.02911/) (2018)
- [Modeling Rich Contexts for Sentiment Classification with LSTM](https://mlsanity.com/paper/1605.01478/) (2016)

## Built on by (most cited first, 4 of 4)

- [Images in Language Space: Exploring the Suitability of Large Language Models for Vision & Language Tasks](https://mlsanity.com/paper/2305.13782/) (2023)
- [Holistic Visual-Textual Sentiment Analysis with Prior Models](https://mlsanity.com/paper/2211.12981/) (2022)
- [M2SA: Multimodal and Multilingual Model for Sentiment Analysis of Tweets](https://mlsanity.com/paper/2404.01753/) (2024)
- [An Enhanced Dual Transformer Contrastive Network for Multimodal Sentiment Analysis](https://mlsanity.com/paper/2510.23617/) (2025)

## Abstract (arXiv)

Opinion and sentiment analysis is a vital task to characterize subjective information in social media posts. In this paper, we present a comprehensive experimental evaluation and comparison with six state-of-the-art methods, from which we have re-implemented one of them. In addition, we investigate different textual and visual feature embeddings that cover different aspects of the content, as well as the recently introduced multimodal CLIP embeddings. Experimental results are presented for two different publicly available benchmark datasets of tweets and corresponding images. In contrast to the evaluation methodology of previous work, we introduce a reproducible and fair evaluation scheme to make results comparable. Finally, we conduct an error analysis to outline the limitations of the methods and possibilities for the future work.