# Beneath the Tip of the Iceberg: Current Challenges and New Directions in Sentiment Analysis Research

arXiv: https://arxiv.org/abs/2005.00357 · HTML: https://mlsanity.com/paper/2005.00357/

## Key facts

- Cited by 67 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 May 2020.

## Built on by (most cited first, 30 of 67)

- [Sentiment Analysis in the Era of Large Language Models: A Reality Check](https://mlsanity.com/paper/2305.15005/) (2023)
- [Learning Modality-Specific Representations with Self-Supervised Multi-Task Learning for Multimodal Sentiment Analysis](https://mlsanity.com/paper/2102.04830/) (2021)
- [Improving Multimodal Fusion with Hierarchical Mutual Information Maximization for Multimodal Sentiment Analysis](https://mlsanity.com/paper/2109.00412/) (2021)
- [TweetNLP: Cutting-Edge Natural Language Processing for Social Media](https://mlsanity.com/paper/2206.14774/) (2022)
- [AfriSenti: A Twitter Sentiment Analysis Benchmark for African Languages](https://mlsanity.com/paper/2302.08956/) (2023)
- [A Survey on Aspect-Based Sentiment Analysis: Tasks, Methods, and Challenges](https://mlsanity.com/paper/2203.01054/) (2022)
- [Make Acoustic and Visual Cues Matter: CH-SIMS v2.0 Dataset and AV-Mixup Consistent Module](https://mlsanity.com/paper/2209.02604/) (2022)
- [Exploiting BERT For Multimodal Target Sentiment Classification Through Input Space Translation](https://mlsanity.com/paper/2108.01682/) (2021)
- [SemEval-2023 Task 12: Sentiment Analysis for African Languages (AfriSenti-SemEval)](https://mlsanity.com/paper/2304.06845/) (2023)
- [Efficient Multimodal Transformer with Dual-Level Feature Restoration for Robust Multimodal Sentiment Analysis](https://mlsanity.com/paper/2208.07589/) (2022)
- [Is BERT Blind? Exploring the Effect of Vision-and-Language Pretraining on Visual Language Understanding](https://mlsanity.com/paper/2303.12513/) (2023)
- [Decoding EEG Brain Activity for Multi-Modal Natural Language Processing](https://mlsanity.com/paper/2102.08655/) (2021)
- [Deep Emotion Recognition in Textual Conversations: A Survey](https://mlsanity.com/paper/2211.09172/) (2022)
- [Aspect Sentiment Triplet Extraction Using Reinforcement Learning](https://mlsanity.com/paper/2108.06107/) (2021)
- [ScaleVLAD: Improving Multimodal Sentiment Analysis via Multi-Scale Fusion of Locally Descriptors](https://mlsanity.com/paper/2112.01368/) (2021)
- [Two Contrasting Data Annotation Paradigms for Subjective NLP Tasks](https://mlsanity.com/paper/2112.07475/) (2021)
- [SANCL: Multimodal Review Helpfulness Prediction with Selective Attention and Natural Contrastive Learning](https://mlsanity.com/paper/2209.05040/) (2022)
- [Through the Lens of Core Competency: Survey on Evaluation of Large Language Models](https://mlsanity.com/paper/2308.07902/) (2023)
- [Shared and Private Information Learning in Multimodal Sentiment Analysis with Deep Modal Alignment and Self-supervised Multi-Task Learning](https://mlsanity.com/paper/2305.08473/) (2023)
- [Financial Sentiment Analysis on News and Reports Using Large Language Models and FinBERT](https://mlsanity.com/paper/2410.01987/) (2024)
- [Multi-Task Learning and Adapted Knowledge Models for Emotion-Cause Extraction](https://mlsanity.com/paper/2106.09790/) (2021)
- [KNOT: Knowledge Distillation using Optimal Transport for Solving NLP Tasks](https://mlsanity.com/paper/2110.02432/) (2021)
- [Towards an Enhanced Understanding of Bias in Pre-trained Neural Language Models: A Survey with Special Emphasis on Affective Bias](https://mlsanity.com/paper/2204.10365/) (2022)
- [Bias Beyond English: Counterfactual Tests for Bias in Sentiment Analysis in Four Languages](https://mlsanity.com/paper/2305.11673/) (2023)
- [Deciphering Political Entity Sentiment in News with Large Language Models: Zero-Shot and Few-Shot Strategies](https://mlsanity.com/paper/2404.04361/) (2024)
- [RoBERTa-BiLSTM: A Context-Aware Hybrid Model for Sentiment Analysis](https://mlsanity.com/paper/2406.00367/) (2024)
- [Multimodal Contrastive Learning via Uni-Modal Coding and Cross-Modal Prediction for Multimodal Sentiment Analysis](https://mlsanity.com/paper/2210.14556/) (2022)
- [Learning a General Clause-to-Clause Relationships for Enhancing Emotion-Cause Pair Extraction](https://mlsanity.com/paper/2208.13549/) (2022)
- [Denoising Bottleneck with Mutual Information Maximization for Video Multimodal Fusion](https://mlsanity.com/paper/2305.14652/) (2023)
- [Triple Disentangled Representation Learning for Multimodal Affective Analysis](https://mlsanity.com/paper/2401.16119/) (2024)

## Abstract (arXiv)

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 marketing, risk management, market research, and politics, to name a few. Given its saturation in specific subtasks -- such as sentiment polarity classification -- and datasets, there is an underlying perception that this field has reached its maturity. In this article, we discuss this perception by pointing out the shortcomings and under-explored, yet key aspects of this field that are necessary to attain true sentiment understanding. We analyze the significant leaps responsible for its current relevance. Further, we attempt to chart a possible course for this field that covers many overlooked and unanswered questions.