# Exploiting BERT for End-to-End Aspect-based Sentiment Analysis

arXiv: https://arxiv.org/abs/1910.00883 · HTML: https://mlsanity.com/paper/1910.00883/

## Key facts

- Cited by 57 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Oct 2019.

## Built on by (most cited first, 30 of 57)

- [Recent Advances in Natural Language Processing via Large Pre-Trained Language Models: A Survey](https://mlsanity.com/paper/2111.01243/) (2021)
- [Aspect Sentiment Quad Prediction as Paraphrase Generation](https://mlsanity.com/paper/2110.00796/) (2021)
- [Is ChatGPT a Good Sentiment Analyzer? A Preliminary Study](https://mlsanity.com/paper/2304.04339/) (2023)
- [A Joint Training Dual-MRC Framework for Aspect Based Sentiment Analysis](https://mlsanity.com/paper/2101.00816/) (2021)
- [A Survey on Aspect-Based Sentiment Analysis: Tasks, Methods, and Challenges](https://mlsanity.com/paper/2203.01054/) (2022)
- [Exploiting BERT For Multimodal Target Sentiment Classification Through Input Space Translation](https://mlsanity.com/paper/2108.01682/) (2021)
- [Improving Aspect Sentiment Quad Prediction via Template-Order Data Augmentation](https://mlsanity.com/paper/2210.10291/) (2022)
- [Structured Sentiment Analysis as Dependency Graph Parsing](https://mlsanity.com/paper/2105.14504/) (2021)
- [GenKubeSec: LLM-Based Kubernetes Misconfiguration Detection, Localization, Reasoning, and Remediation](https://mlsanity.com/paper/2405.19954/) (2024)
- [Challenges for Open-domain Targeted Sentiment Analysis](https://mlsanity.com/paper/2204.06893/) (2022)
- [Vietnamese Hate and Offensive Detection using PhoBERT-CNN and Social Media Streaming Data](https://mlsanity.com/paper/2206.00524/) (2022)
- [Bangla Natural Language Processing: A Comprehensive Analysis of Classical, Machine Learning, and Deep Learning Based Methods](https://mlsanity.com/paper/2105.14875/) (2021)
- [Evaluating Pre-Trained Models for User Feedback Analysis in Software Engineering: A Study on Classification of App-Reviews](https://mlsanity.com/paper/2104.05861/) (2021)
- [Deep Transfer Learning & Beyond: Transformer Language Models in Information Systems Research](https://mlsanity.com/paper/2110.08975/) (2021)
- [A Dataset and BERT-based Models for Targeted Sentiment Analysis on Turkish Texts](https://mlsanity.com/paper/2205.04185/) (2022)
- [EFSA: Towards Event-Level Financial Sentiment Analysis](https://mlsanity.com/paper/2404.08681/) (2024)
- [Towards Unifying the Label Space for Aspect- and Sentence-based Sentiment Analysis](https://mlsanity.com/paper/2203.07090/) (2022)
- [CL-XABSA: Contrastive Learning for Cross-lingual Aspect-based Sentiment Analysis](https://mlsanity.com/paper/2204.00791/) (2022)
- [Artificial intelligence for topic modelling in Hindu philosophy: mapping themes between the Upanishads and the Bhagavad Gita](https://mlsanity.com/paper/2205.11020/) (2022)
- [Zero-Shot Aspect-Based Sentiment Analysis](https://mlsanity.com/paper/2202.01924/) (2022)
- [Revisit Few-shot Intent Classification with PLMs: Direct Fine-tuning vs. Continual Pre-training](https://mlsanity.com/paper/2306.05278/) (2023)
- [Which side are you on? Insider-Outsider classification in conspiracy-theoretic social media](https://mlsanity.com/paper/2203.04356/) (2022)
- [Disentangled Learning of Stance and Aspect Topics for Vaccine Attitude Detection in Social Media](https://mlsanity.com/paper/2205.03296/) (2022)
- [Czech Dataset for Complex Aspect-Based Sentiment Analysis Tasks](https://mlsanity.com/paper/2508.08125/) (2025)
- [Arabic aspect based sentiment analysis using bidirectional GRU based models](https://mlsanity.com/paper/2101.10539/) (2021)
- [Emotion Analysis using Multi-Layered Networks for Graphical Representation of Tweets](https://mlsanity.com/paper/2207.00907/) (2022)
- [PerPLM: Personalized Fine-tuning of Pretrained Language Models via Writer-specific Intermediate Learning and Prompts](https://mlsanity.com/paper/2309.07727/) (2023)
- [Re-Evaluating GermEval17 Using German Pre-Trained Language Models](https://mlsanity.com/paper/2102.12330/) (2021)
- [Cost-effective Deployment of BERT Models in Serverless Environment](https://mlsanity.com/paper/2103.10673/) (2021)
- [Detecting Conspiracy Theory Against COVID-19 Vaccines](https://mlsanity.com/paper/2211.13003/) (2022)

## Abstract (arXiv)

In this paper, we investigate the modeling power of contextualized embeddings from pre-trained language models, e.g. BERT, on the E2E-ABSA task. Specifically, we build a series of simple yet insightful neural baselines to deal with E2E-ABSA. The experimental results show that even with a simple linear classification layer, our BERT-based architecture can outperform state-of-the-art works. Besides, we also standardize the comparative study by consistently utilizing a hold-out validation dataset for model selection, which is largely ignored by previous works. Therefore, our work can serve as a BERT-based benchmark for E2E-ABSA.