# Challenges for Open-domain Targeted Sentiment Analysis

arXiv: https://arxiv.org/abs/2204.06893 · HTML: https://mlsanity.com/paper/2204.06893/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 48 references that are in the map.
- First posted on arXiv 14 Apr 2022.

## Builds on (most cited first, 17 of 17)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://mlsanity.com/paper/1910.13461/) (2019)
- [Multilingual Denoising Pre-training for Neural Machine Translation](https://mlsanity.com/paper/2001.08210/) (2020)
- [Order Matters: Sequence to sequence for sets](https://mlsanity.com/paper/1511.06391/) (2015)
- [A Unified Generative Framework for Aspect-Based Sentiment Analysis](https://mlsanity.com/paper/2106.04300/) (2021)
- [Exploiting BERT for End-to-End Aspect-based Sentiment Analysis](https://mlsanity.com/paper/1910.00883/) (2019)
- [A Unified Model for Opinion Target Extraction and Target Sentiment Prediction](https://mlsanity.com/paper/1811.05082/) (2018)
- [Open-Domain Targeted Sentiment Analysis via Span-Based Extraction and Classification](https://mlsanity.com/paper/1906.03820/) (2019)
- [SentiHood: Targeted Aspect Based Sentiment Analysis Dataset for Urban Neighbourhoods](https://mlsanity.com/paper/1610.03771/) (2016)
- [Solving Aspect Category Sentiment Analysis as a Text Generation Task](https://mlsanity.com/paper/2110.07310/) (2021)
- [GRACE: Gradient Harmonized and Cascaded Labeling for Aspect-based Sentiment Analysis](https://mlsanity.com/paper/2009.10557/) (2020)
- [Lifelong Learning CRF for Supervised Aspect Extraction](https://mlsanity.com/paper/1705.00251/) (2017)
- [YASO: A Targeted Sentiment Analysis Evaluation Dataset for Open-Domain Reviews](https://mlsanity.com/paper/2012.14541/) (2020)
- [Human-Like Decision Making: Document-level Aspect Sentiment Classification via Hierarchical Reinforcement Learning](https://mlsanity.com/paper/1910.09260/) (2019)
- [Towards Target-dependent Sentiment Classification in News Articles](https://mlsanity.com/paper/2105.09660/) (2021)
- [Jointly Modeling Aspect and Sentiment with Dynamic Heterogeneous Graph Neural Networks](https://mlsanity.com/paper/2004.06427/) (2020)

## Built on by (most cited first, 9 of 9)

- [Is ChatGPT a Good Sentiment Analyzer? A Preliminary Study](https://mlsanity.com/paper/2304.04339/) (2023)
- [Both Matter: Enhancing the Emotional Intelligence of Large Language Models without Compromising the General Intelligence](https://mlsanity.com/paper/2402.10073/) (2024)
- [Entity-Level Sentiment Analysis (ELSA): An exploratory task survey](https://mlsanity.com/paper/2304.14241/) (2023)
- [Cross-domain Generalization for AMR Parsing](https://mlsanity.com/paper/2210.12445/) (2022)
- [A Multilingual Similarity Dataset for News Article Frame](https://mlsanity.com/paper/2405.13272/) (2024)
- [XAL: EXplainable Active Learning Makes Classifiers Better Low-resource Learners](https://mlsanity.com/paper/2310.05502/) (2023)
- [FaiMA: Feature-aware In-context Learning for Multi-domain Aspect-based Sentiment Analysis](https://mlsanity.com/paper/2403.01063/) (2024)
- [EmoGRACE: Aspect-based emotion analysis for social media data](https://mlsanity.com/paper/2503.15133/) (2025)
- [DanceHA: A Multi-Agent Framework for Document-Level Aspect-Based Sentiment Analysis](https://mlsanity.com/paper/2603.16546/) (2026)

## Abstract (arXiv)

Since previous studies on open-domain targeted sentiment analysis are limited in dataset domain variety and sentence level, we propose a novel dataset consisting of 6,013 human-labeled data to extend the data domains in topics of interest and document level. Furthermore, we offer a nested target annotation schema to extract the complete sentiment information in documents, boosting the practicality and effectiveness of open-domain targeted sentiment analysis. Moreover, we leverage the pre-trained model BART in a sequence-to-sequence generation method for the task. Benchmark results show that there exists large room for improvement of open-domain targeted sentiment analysis. Meanwhile, experiments have shown that challenges remain in the effective use of open-domain data, long documents, the complexity of target structure, and domain variances.