ASPECT-BASED SENTIMENT ANALYSIS

GRACE: Gradient Harmonized and Cascaded Labeling for Aspect-based Sentiment Analysis (2020.findings-emnlp)

Challenge:	Existing methods to train ABSA model are limited by lack of annotated data . a dual-granularity pseudo labeling approach is proposed to solve this problem .
Approach:	They propose a framework for aspect-based sentiment analysis that uses annotated data to train ABSA models.
Outcome:	The proposed framework surpasses previous methods on benchmarks.

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Huaishao Luo, Lei Ji, Tianrui Li, Daxin Jiang, Nan Duan

Aspect Extraction Using Coreference Resolution and Unsupervised Filtering (2020.aacl-srw)

Challenge:	Existing studies ignore aspect terms interaction when labeling polarities . aspect terms extraction and aspect sentiment classification are two fundamental tasks .
Approach:	They propose a GRadient hArmonized and CascadEd labeling model to solve the imbalance issue . they extend the gradient harmonized mechanism used in object detection to aspect-based sentiment analysis .
Outcome:	The proposed model achieves consistency improvement on multiple benchmark datasets and generates state-of-the-art results.

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Deon Mai, Wei Emma Zhang

A Robustly Optimized BMRC for Aspect Sentiment Triplet Extraction (2022.naacl-main)

Challenge:	Existing approaches to extract aspects from text are supervised and unsupervised . experimental results show that unsupervised approaches are more accurate than supervised ones .
Approach:	They propose to combine a lexical rule-based approach with coreference resolution to improve accuracy.
Outcome:	The proposed approach outperforms baseline methods on two benchmark datasets.

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Shu Liu, Kaiwen Li, Zuhe Li

Robustness in NLP

Challenge:	Aspect sentiment triplet extraction (ASTE) is a challenging subtask in aspect-based sentiment analysis.
Approach:	They propose a bidirectional machine reading comprehension method to extract triplets of aspects, opinions and sentiments with complex correspondence from the context.
Outcome:	The proposed method achieves state-of-the-art on multiple benchmark datasets.

Understanding Pre-trained BERT for Aspect-based Sentiment Analysis (2020.coling-main)

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Hu Xu, Lei Shu, Philip Yu, Bing Liu

Aspect-Category-Opinion-Sentiment Quadruple Extraction with Implicit Aspects and Opinions (2021.acl-long)

Challenge:	Recent studies show impressive results on aspects-based sentiment analysis tasks.
Approach:	They analyze the attentions and learned representations of BERT for aspects-based sentiment analysis tasks.
Outcome:	The proposed model can be used for aspects-based sentiment analysis (ABSA) but it is not clear how it can provide important features for downstream tasks.

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Hongjie Cai, Rui Xia, Jianfei Yu

End-to-End Aspect-Guided Review Summarization at Scale (2025.emnlp-industry)

Challenge:	Existing studies in aspect-based sentiment analysis ignore aspects and opinions in product reviews.
Approach:	They propose a task to extract aspect-category-opinion-sentiment quadruples from review sentences . they construct two new datasets that contain annotations of implicit aspects and opinions .
Outcome:	The proposed task provides full support for aspect-based sentiment analysis with implicit aspects and opinions.

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Ilya Boytsov, Vinny DeGenova, Mikhail Balyasin, Joseph Walt, Caitlin Eusden, Marie-Claire Rochat, Margaret Pierson

Summarization

Challenge:	Existing methods to generate concise product review summaries are prone to hallucination, omission of important facts, and factual errors.
Approach:	They propose a large language model-based system that combines aspect-based sentiment analysis with guided summarization to generate concise product review summaries.
Outcome:	The proposed system generates concise and interpretable product review summaries using a large language model (LLM) dataset.

Recurrent Entity Networks with Delayed Memory Update for Targeted Aspect-Based Sentiment Analysis (N18-2)

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Fei Liu, Trevor Cohn, Timothy Baldwin

DOER: Dual Cross-Shared RNN for Aspect Term-Polarity Co-Extraction (P19-1)

Challenge:	Recent work on target-dependent biLSTMs has shown that they are ineffective in aspect-based sentiment analysis.
Approach:	They propose a novel architecture that uses external memory chains with a delayed memory update mechanism to track entities.
Outcome:	The proposed model improves on a TABSA task using external memory chains with a delayed memory update mechanism.

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Huaishao Luo, Tianrui Li, Bing Liu, Junbo Zhang

A Generative Language Model for Few-shot Aspect-Based Sentiment Analysis (2022.findings-naacl)

Challenge:	Existing algorithms address aspect term extraction and aspect sentiment classification as separate tasks, which can be complicated for real applications.
Approach:	They propose a dual crOss-sharEd RNN framework to generate all aspect term-polarity pairs of the input sentence simultaneously.
Outcome:	The proposed framework outperforms state-of-the-art frameworks on three benchmark datasets.

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Ehsan Hosseini-Asl, Wenhao Liu, Caiming Xiong

Aspect-Based Emotion Analysis and Multimodal Coreference: A Case Study of Customer Comments on Adidas Instagram Posts (2022.lrec-1)

Challenge:	Pre-trained language models are often used to achieve state-of-the-art results . eval paper shows that generative language model can handle joint and multi-task settings .
Approach:	They propose to reformulate extraction and prediction tasks into a sequence generation task . they propose a generative language model with unidirectional attention that learns to accomplish the tasks via language generation .
Outcome:	The proposed model outperforms the state-of-the-art in few-shot and full-shot settings.

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Luna De Bruyne, Akbar Karimi, Orphee De Clercq, Andrea Prati, Veronique Hoste

Visual Data in NLP

Challenge:	Aspect-based sentiment analysis of user-generated content has been relatively unexplored in recent years.
Approach:	They present a multimodal dataset for Aspect-Based Emotion Analysis (ABEA) they take the first steps in investigating the utility of multimodal coreference resolution in an ABEA framework.
Outcome:	The proposed dataset consists of 4,900 comments on 175 images and is annotated with aspect and emotion categories and the emotional dimensions of valence and arousal.

Deep Context- and Relation-Aware Learning for Aspect-based Sentiment Analysis (2021.acl-short)

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Shinhyeok Oh, Dongyub Lee, Taesun Whang, IlNam Park, Seo Gaeun, EungGyun Kim, Harksoo Kim

Label Correction Model for Aspect-based Sentiment Analysis (2020.coling-main)

Challenge:	Existing methods for aspect-based sentiment analysis (ABSA) consider relationships implicitly among subtasks at the word level.
Approach:	They propose a deep contextualized relation-aware network that allows interactive relations among subtasks . they propose self-supervised strategies that deal with multiple aspects .
Outcome:	The proposed method outperforms state-of-the-art methods on three widely used benchmarks.

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Qianlong Wang, Jiangtao Ren

Adaptive Structure Induction for Aspect-based Sentiment Analysis with Spectral Perspective (2023.findings-emnlp)

Challenge:	Existing models for aspect-based sentiment analysis ignore a phenomenon: aspect boundary label and sentiment label can correct each other.
Approach:	They propose a model that uses aspect boundary label and sentiment label to correct each other . they evaluate the model on three benchmark datasets and evaluate its performance .
Outcome:	The proposed model performs state-of-the-art on three benchmark datasets.

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Hao Niu, Yun Xiong, Xiaosu Wang, Wenjing Yu, Yao Zhang, Zhonglei Guo

ASTE-Transformer: Modelling Dependencies in Aspect-Sentiment Triplet Extraction (2024.findings-emnlp)

Challenge:	incorporating structure information can enhance the performance of aspect-based sentiment analysis.
Approach:	They propose to use pre-trained language models to induct latent structures from a spectrum perspective.
Outcome:	The proposed model shortens Aspects-sentiment Distance and improves structure induction ability.

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Iwo Naglik, Mateusz Lango

Joint Aspect and Polarity Classification for Aspect-based Sentiment Analysis with End-to-End Neural Networks (D18-1)

Challenge:	Aspect-Sentiment Triplet Extraction (ASTE) is a recent task in aspect-based sentiment analysis.
Approach:	They propose a task of aspect-based sentiment analysis that extracts triples from sentences . they propose three transformer-inspired layers to enable modelling of dependencies .
Outcome:	The proposed method achieves higher performance in terms of F1 measure than other methods studied on popular benchmarks.

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Martin Schmitt, Simon Steinheber, Konrad Schreiber, Benjamin Roth

DomBERT: Domain-oriented Language Model for Aspect-based Sentiment Analysis (2020.findings-emnlp)

Challenge:	a new approach for aspect-based sentiment analysis is proposed . we compare the performance of the proposed approach with pipeline approaches .
Approach:	They propose a model for aspect-based sentiment analysis that uses a convolutional neural network and fasttext embeddings to combine the two approaches.
Outcome:	The proposed model outperforms pipeline approaches in aspects-based sentiment analysis.

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Hu Xu, Bing Liu, Lei Shu, Philip Yu

A Weak Supervision Approach for Few-Shot Aspect Based Sentiment Analysis (2024.eacl-long)

Challenge:	Recent studies show that learning domain-specific language models are equally important for general-purpose and domain-based learning.
Approach:	They propose a domain-oriented learning task that combine the benefits of both general and domain-specific worlds.
Outcome:	The proposed task solves the problems in an aspect-based sentiment analysis task.

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Robert Vacareanu, Siddharth Varia, Kishaloy Halder, Shuai Wang, Giovanni Paolini, Neha Anna John, Miguel Ballesteros, Smaranda Muresan

Attention-based Relational Graph Convolutional Network for Target-Oriented Opinion Words Extraction (2021.eacl-main)

Challenge:	Existing methods to improve few-shot performance in aspect-based sentiment analysis (ABSA) require complex interactions between the target and the polarity of the sentiment.
Approach:	They propose a pipeline approach to construct a noisy ABSA dataset and adapt it to the ABSA tasks.
Outcome:	The proposed model outperforms the state-of-the-art on the aspect extraction sentiment classification task and is capable of performing the harder aspect sentiment triplet extraction task.

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Junfeng Jiang, An Wang, Akiko Aizawa

Multimodality

Challenge:	empirical results show that our model significantly outperforms all existing models on four benchmark datasets.
Approach:	They propose a novel attention-based relational graph convolutional neural network to exploit syntactic information over dependency graphs.
Outcome:	The proposed model outperforms existing models on four benchmark datasets.

Label-Driven Denoising Framework for Multi-Label Few-Shot Aspect Category Detection (2022.findings-emnlp)

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Fei Zhao, Yuchen Shen, Zhen Wu, Xinyu Dai

Responsible & Trustworthy NLP

Challenge:	Existing methods for ACD use label information of aspect categories to detect aspect categories . but, they still suffer from noise problems due to lack of supervised data .
Approach:	They propose a Label-Driven Denoising Framework to alleviate noise problems for ACD subtask . they use the label information of each aspect to generate a better prototype .
Outcome:	The proposed framework improves the performance of the multi-label few-shot Aspect Category Detection task.

Reducing Spurious Correlations in Aspect-based Sentiment Analysis with Explanation from Large Language Models (2023.findings-emnlp)

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Qianlong Wang, Keyang Ding, Bin Liang, Min Yang, Ruifeng Xu

Responsible & Trustworthy NLP

Challenge:	Aspect-based sentiment analysis models are susceptible to learning spurious correlations between words . a recent study shows that feature engineering is time-consuming and costly .
Approach:	They propose to use a template to prompt LLMs to generate an appropriate explanation for the sentiment polarity of each aspect to reduce spurious correlations.
Outcome:	The proposed methods improve ABSA models and their generalization ability.

Aspect-Based Sentiment Analysis with Syntax-Opinion-Sentiment Reasoning Chain (2025.coling-main)

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Rui Fan, Shu Li, Tingting He, Yu Liu

Using Review Combination and Pseudo-Tokens for Aspect Sentiment Quad Prediction (2025.findings-naacl)

Challenge:	Syntactic structures are crucial for capturing aspect-opinion relationships . syntactically based models struggle with linguistic complexities .
Approach:	They propose a syntactic-opinion-sentiment reasoning framework that leverages syntaktic information to improve ABSA performance.
Outcome:	The proposed framework improves ABSA performance, though smaller LLMs exhibit weaker performance.

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Jiazhou Chen, Xu Jia, RuiQiang Guo

Aspect-based Sentiment Analysis as Machine Reading Comprehension (2022.coling-1)

Challenge:	Existing models confuse implicit and explicit sentiment, making it difficult to extract quadruples effectively.
Approach:	They propose a framework that leverages distinct labeled features from diverse reviews and incorporates pseudo-token prompts to harness the semantic knowledge of pre-trained models.
Outcome:	The proposed framework improves over four public datasets, averaging 1.99% F1 improvement, particularly in instances involving implicit sentiment.

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Yifei Yang, Hai Zhao

Reasoning

Challenge:	Existing approaches to aspect-based sentiment analysis stack multiple modules and result in severe error propagation.
Approach:	They propose a MRC-PrOmpt mOdeL framework where multiple sentiment aspects are elicited by a machine reading comprehension model and their corresponding sentiment polarities are classified in a prompt learning way.
Outcome:	The proposed framework significantly outperforms existing state-of-the-art models or achieves comparable performance on widely-used benchmark datasets.

Pinpointing Diffusion Grid Noise to Enhance Aspect Sentiment Quad Prediction (2024.findings-acl)

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Linan Zhu, Xiangfan Chen, Xiaolei Guo, Chenwei Zhang, Zhechao Zhu, Zehai Zhou, Xiangjie Kong

Aspect-based Sentiment Analysis with Type-aware Graph Convolutional Networks and Layer Ensemble (2021.naacl-main)

Challenge:	Current studies on aspect-based sentiment analysis focus on essential content for model generation, ignoring the incorporation of various noise during training.
Approach:	They propose a grid noise-diffusion pinpoint network (GDP) model that incorporates three new modules to tackle generation instability.
Outcome:	The proposed model reduces the generation instability of model learning and outputs by incorporating Consistency Likelihood Learning and GDP-FOR.

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Yuanhe Tian, Guimin Chen, Yan Song

MvP: Multi-view Prompting Improves Aspect Sentiment Tuple Prediction (2023.acl-long)

Challenge:	Existing studies only leverage dependency relations without considering their dependency types . a valid and effective approach is demonstrated on six English benchmark datasets .
Approach:	They propose to explicitly utilize dependency types for ABSA with type-aware graph convolutional networks . attention is used in T-GCN to distinguish different edges in the graph and attentive layer ensemble to comprehensively learn from different layers of T-gCN.
Outcome:	The proposed approach performs well on six English benchmark datasets.

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Zhibin Gou, Qingyan Guo, Yujiu Yang

BERT Post-Training for Review Reading Comprehension and Aspect-based Sentiment Analysis (N19-1)

Challenge:	Existing studies predict sentiment elements in a fixed order, which ignores the interdependence of the elements and the diversity of language expression.
Approach:	They propose a multi-view process that aggregates sentiment elements generated in different order . they use element order prompts to guide the language model to generate multiple tuples with different element order based on a given text .
Outcome:	The proposed method outperforms existing methods on 10 datasets of 4 benchmark tasks and is highly flexible and transferable across tasks.

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Hu Xu, Bing Liu, Lei Shu, Philip Yu

Sentiment Analysis in the Era of Large Language Models: A Reality Check (2024.findings-naacl)

Challenge:	Existing work on question-answering has limited training examples for RRC . question-announced questions are a key component of online commerce .
Approach:	They propose to turn customer reviews into a large source of knowledge that can be exploited to answer user questions.
Outcome:	The proposed approach improves review reading comprehension on popular language model BERT . it also improves aspect extraction and aspect sentiment classification tasks .

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Wenxuan Zhang, Yue Deng, Bing Liu, Sinno Pan, Lidong Bing

Eliminating Sentiment Bias for Aspect-Level Sentiment Classification with Unsupervised Opinion Extraction (2021.findings-emnlp)

Challenge:	Sentiment analysis (SA) has been a long-standing research area in natural language processing.
Approach:	They propose a benchmark to evaluate LLMs' SA abilities and propose 'sentiEval' benchmark to be used for a more comprehensive evaluation.
Outcome:	The proposed benchmark outperforms small language models on 26 datasets on 13 tasks and compared them with LLMs trained on domain-specific datasets.

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Bo Wang, Tao Shen, Guodong Long, Tianyi Zhou, Yi Chang

Modelling Context and Syntactical Features for Aspect-based Sentiment Analysis (2020.acl-main)

Challenge:	Aspect-level sentiment classification (ALSC) is a practical setting in aspect-based sentiment analysis due to no opinion term labeling needed, but it fails to interpret why a sentiment polarity is derived for the aspect.
Approach:	They propose a span-based anti-bias aspect representation learning framework that eliminates the sentiment bias in the aspect embedding by adversarial learning against aspects’ prior sentiment.
Outcome:	The proposed framework achieves state-of-the-art performance on five benchmarks, with the capability of unsupervised opinion extraction.

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Minh Hieu Phan, Philip O. Ogunbona

Exploring Multilingual Pre-trained Language Model for Aspect-based Sentiment Analysis (2026.findings-acl)

Challenge:	Existing approaches to aspect-based sentiment analysis do not fully leverage syntactical information.
Approach:	They propose an end-to-end aspect-based sentiment analysis solution that integrates syntactical information with part-of-speech embeddings and dependency-based embeddables to enhance the performance of the aspect extractor.
Outcome:	The proposed solution outperforms the state-of-the-art models on SemEval-2014 dataset in both subtasks.

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Ye Wang, Ruijun Jiang, Zhongqing Wang, Guodong Zhou

To be Closer: Learning to Link up Aspects with Opinions (2021.emnlp-main)

Challenge:	Aspect-based sentiment analysis studies have focused on English datasets, but labeled data is scarce.
Approach:	They propose a multilingual pre-trained language model that leverages bilingual pre-training to leverage aspects-based sentiment analysis.
Outcome:	The proposed model outperforms state-of-the-art models across multiple languages.

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Yuxiang Zhou, Lejian Liao, Yang Gao, Zhanming Jie, Wei Lu

Semantics-Preserved Data Augmentation for Aspect-Based Sentiment Analysis (2021.emnlp-main)

Challenge:	Dependency parsers are not designed for capturing interaction between opinion words and aspect words.
Approach:	They propose to learn an aspect-centric tree structure to shorten distance between aspects and opinion words.
Outcome:	The proposed model outperforms baselines on five aspect-based sentiment datasets.

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Ting-Wei Hsu, Chung-Chi Chen, Hen-Hsen Huang, Hsin-Hsi Chen

Czech Dataset for Complex Aspect-Based Sentiment Analysis Tasks (2024.lrec-main)

Challenge:	Existing methods for data augmentation address data deficiencies and semantic consistency, but they ignore the second issue.
Approach:	They propose a semantics-preserving data augmentation approach that preserves the semantics of a textual sequence.
Outcome:	The proposed method achieves better performance on publicly available datasets and stock price/risk movement prediction scenarios.

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Jakub Šmíd, Pavel Přibáň, Ondrej Prazak, Pavel Kral

Benchmark Creation for Aspect-Based Sentiment Analysis in Low-Resource Odia Language and Evaluation through Fine-Tuning of Multilingual Models (2025.coling-main)

Challenge:	3.1K reviews are manually annotated for aspect-based sentiment analysis (ABSA) ABSA is a fine-grained task that aims to identify the sentiment associated with each aspect or characteristic of a text.
Approach:	They propose a new Czech dataset for aspect-based sentiment analysis . the new dataset is built upon the older Czech dataset . authors provide 24M reviews without annotations suitable for unsupervised learning .
Outcome:	The proposed dataset is built upon the older dataset, but is specifically designed for more complex tasks.

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Lipika Dewangan, Zoyah Afsheen Sayeed, Chandresh Maurya

Debiasing Multi-Entity Aspect-Based Sentiment Analysis with Norm-Based Data Augmentation (2024.lrec-main)

Challenge:	Aspect-based sentiment analysis is underexplored in low-resource languages such as Odia . a dataset is annotated for two tasks: Aspect Term Extraction (ATE) and Aspect Polarity Classification (APC)
Approach:	They propose to use a dataset for aspect-based sentiment analysis in Odia . they use ensemble data augmentation and a fine-tuned paraphrase generation model .
Outcome:	The proposed dataset is annotated for two tasks: ATE and APC . the proposed dataset will spur more work for the ABSA task in Odia .

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Scott Friedman, Joan Zheng, Hillel Steinmetz

Responsible & Trustworthy NLP

Challenge:	Recent research has explored strategies for reduce measurable biases in NLP predictions while maintaining prediction accuracy on held-out test sets.
Approach:	They propose to augment training data with norm-based language templates derived from previous language resources to reduce biases in NLP models.
Outcome:	The proposed model reduces topical bias to less than half while maintaining prediction quality on held-out test sets.

Evaluating Methods for Extraction of Aspect Terms in Opinion Texts in Portuguese - the Challenges of Implicit Aspects (2022.lrec-1)

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Mateus Machado, Thiago Alexandre Salgueiro Pardo

An Instruction Tuning-Based Contrastive Learning Framework for Aspect Sentiment Quad Prediction with Implicit Aspects and Opinions (2024.findings-emnlp)

Challenge:	In aspect-based sentiment analysis, the implicit mention of aspects is difficult to identify and may require world knowledge to do so.
Approach:	They evaluate frequency-based, hybrid, and machine learning methods to extract aspect terms from opinionated texts in Portuguese.
Outcome:	The proposed methods show that they are more efficient and more efficient than previous methods.

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Hao Zhang, Yu-N Cheah, Congqing He, Feifan Yi

Context-aware Embedding for Targeted Aspect-based Sentiment Analysis (P19-1)

Challenge:	Existing methods for aspect-based sentiment analysis have not explored how to effectively leverage the knowledge of pre-trained language models to handle implicit aspects and opinions.
Approach:	They propose a framework leveraging Instruction Tuning and Supervised Contrastive Learning to improve aspect sentiment quad prediction for implicit aspects and opinions.
Outcome:	The proposed framework significantly outperforms existing methods on benchmark datasets.

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Bin Liang, Jiachen Du, Ruifeng Xu, Binyang Li, Hejiao Huang

Representation Learning

Challenge:	Existing methods do not specifically pre-train reasonable embeddings for targets and aspects in TABSA.
Approach:	They propose to refine the embeddings of targets and aspects using a sparse coefficient vector . this allows the embeds to be refined from highly correlative words instead of context-independent vectors .
Outcome:	Experiments show that the proposed method improves on two benchmark datasets.

A Sequence-to-Structure Approach to Document-level Targeted Sentiment Analysis (2023.findings-emnlp)

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Nan Song, Hongjie Cai, Rui Xia, Jianfei Yu, Zhen Wu, Xinyu Dai

AoM: Detecting Aspect-oriented Information for Multimodal Aspect-Based Sentiment Analysis (2023.findings-acl)

Challenge:	Aspect-based sentiment analysis (ABSA) has received wide attention in NLP for nearly two decades . previous studies focused on sentence-level ABSA, but document-level research has not received enough attention.
Approach:	They propose a Sequence-to-Structure approach to address the document-level targeted sentiment analysis task, which aims to extract the opinion targets consisting of multi-level entities from a review document and predict their sentiments.
Outcome:	The proposed approach outperforms baselines on six domains on the document-level targeted sentiment analysis task.

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Ru Zhou, Wenya Guo, Xumeng Liu, Shenglong Yu, Ying Zhang, Xiaojie Yuan

Using Text-Based Causal Inference to Disentangle Factors Influencing Online Review Ratings (2025.naacl-long)

Challenge:	Existing methods to extract aspects from text-image pairs and recognize their sentiments are noisy and coarsely establishing image-aspect alignment will interfere with aspect-relevant semantic and sentiment information.
Approach:	They propose an Aspect-oriented method to detect aspect-relevant semantic and sentiment information by selecting textual tokens and image blocks that are semantically related to the aspects.
Outcome:	The proposed method is superior to existing methods in the field of sentiment analysis.

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Linsen Li, Aron Culotta, Nicholas Mattei

Weakly-Supervised Aspect-Based Sentiment Analysis via Joint Aspect-Sentiment Topic Embedding (2020.emnlp-main)

Challenge:	Existing methods to analyze online reviews for aspects of quality are limited . authors propose a method to disentangle the impact of each aspect on overall perception .
Approach:	They propose a method to disentangle the effect of each aspect on overall perception . they use textual mentions in reviews as proxies for real-world attributes .
Outcome:	The proposed method improves on real-world reviews of U.S. K-12 schools.

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Jiaxin Huang, Yu Meng, Fang Guo, Heng Ji, Jiawei Han

Unified Feature and Instance Based Domain Adaptation for Aspect-Based Sentiment Analysis (2020.emnlp-main)

Challenge:	Existing methods for aspect-based sentiment analysis of review text use only a few keywords describing each aspect/sentiment without using any labeled examples.
Approach:	They propose a weakly-supervised approach for aspect-based sentiment analysis which uses only a few keywords describing each aspect/sentiment without using any labeled examples.
Outcome:	The proposed method generates quality joint topics and outperforms baselines significantly on benchmark datasets.

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Chenggong Gong, Jianfei Yu, Rui Xia

Learning from Adjective-Noun Pairs: A Knowledge-enhanced Framework for Target-Oriented Multimodal Sentiment Classification (2022.coling-1)

Challenge:	Existing approaches to aspect-based sentiment analysis rely on labeled data, but they lack the fine-grained labeles needed for the ABSA task.
Approach:	They propose a framework to perform feature adaptation and instance adaptation for the ABSA task . they learn domain-invariant feature representations by using part-of-speech features .
Outcome:	The proposed method improves on the state-of-the-art in two aspects of the ABSA task.

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Fei Zhao, Zhen Wu, Siyu Long, Xinyu Dai, Shujian Huang, Jiajun Chen

Multimodality

Challenge:	Existing methods to determine sentiment polarity of opinion target are inconsistent and lack visual attention.
Approach:	They propose a framework which can exploit adjective-noun pairs extracted from images to improve visual attention and sentiment prediction capability of the TMSC task.
Outcome:	The proposed framework outperforms state-of-the-art on two public datasets.

Aspect On: an Interactive Solution for Post-Editing the Aspect Extraction based on Online Learning (2020.lrec-1)

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Mara Chinea-Rios, Marc Franco-Salvador, Yassine Benajiba

CACA: Context-Aware Cross-Attention Network for Extractive Aspect Sentiment Quad Prediction (2025.coling-main)

Challenge:	Experimental results show that Aspect On dramatically reduces the number of user clicks and effort required to post-edit the aspects extracted by the model.
Approach:	They propose an online learning-based aspect extraction solution that allows users to post-edit the aspect extraction with little effort.
Outcome:	The proposed solution dramatically reduces the number of user clicks and effort required to post-edit the aspects extracted by the model.

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Bingfeng Chen, Haoran Xu, Yongqi Luo, Boyan Xu, Ruichu Cai, Zhifeng Hao

Self-Training with Pseudo-Label Scorer for Aspect Sentiment Quad Prediction (2024.acl-long)

Challenge:	Existing generative ASQP approaches do not model the contextual relationship of the review sentence to predict implicit terms.
Approach:	They propose an extractive ASQP framework, CACA, which features with Context-Aware Cross-Attention Network to enhance alignment of aspects and opinions.
Outcome:	The proposed framework improves the alignment of aspects and opinions, whether explicit or implicit, and improves on three benchmark datasets.

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Yice Zhang, Jie Zeng, Weiming Hu, Ziyi Wang, Shiwei Chen, Ruifeng Xu

A Joint Coreference-Aware Approach to Document-Level Target Sentiment Analysis (2024.acl-long)

Challenge:	Aspect Sentiment Quad Prediction (ASQP) aims to predict all quads (aspect term, aspect category, opinion term, sentiment polarity) for a given review.
Approach:	They propose a self-training framework with a pseudo-label scorer to assess the match between reviews and their pseudo-labels and train a generative model on it.
Outcome:	The proposed framework can predict all quads (aspect term, aspect category, opinion term, sentiment polarity) for a given review, and it can significantly improve self-training.

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Hongjie Cai, Heqing Ma, Jianfei Yu, Rui Xia

A Large-Scale Japanese Dataset for Aspect-based Sentiment Analysis (2022.lrec-1)

Challenge:	Existing work on aspect-based sentiment analysis (ABSA) focuses on sentence level, document level ABSA is more practical and requires holistic document-level understanding capabilities.
Approach:	They propose a learning framework to jointly model the DTSA task and the coreference resolution task using ChatGPT.
Outcome:	The proposed framework reduces the reliance on annotated coreference information and alleviates evaluation bias caused by missing coreference targets.

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Yuki Nakayama, Koji Murakami, Gautam Kumar, Sudha Bhingardive, Ikuko Hardaway

Pars-ABSA: a Manually Annotated Aspect-based Sentiment Analysis Benchmark on Farsi Product Reviews (2022.lrec-1)

Challenge:	Aspect-based sentiment analysis (ABSA) has not been explored in the Japanese language . there is no standard Japanese dataset available for ABSA task in the language - a paper by cnn.
Approach:	They propose to use a Japanese aspect-based sentiment analysis dataset for hotel reviews domain . they propose to include 53,192 review sentences with seven aspect categories and two polarity labels .
Outcome:	The proposed dataset contains 53,192 review sentences with seven aspect categories and two polarity labels.

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Taha Shangipour ataei, Kamyar Darvishi, Soroush Javdan, Behrouz Minaei-Bidgoli, Sauleh Eetemadi

Aspect-Based Sentiment Analysis as Fine-Grained Opinion Mining (2020.lrec-1)

Challenge:	Existing systems for sentiment analysis are focused on document and sentence levels, but there are no public datasets on aspect-based sentiment analysis for Farsi.
Approach:	They propose to use a manually annotated Farsi dataset to analyze the opinion polarity of reviews . they also use transfer learning to analyze aspects of the review to improve their results .
Outcome:	The proposed method performs better than other aspects of the existing system.

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Gerardo Ocampo Diaz, Xuanming Zhang, Vincent Ng

DiaASQ: A Benchmark of Conversational Aspect-based Sentiment Quadruple Analysis (2023.findings-acl)

Challenge:	a large body of research has been done on aspect-based sentiment analysis (ABSA) for almost two decades . aspect-Based sentiment analysis is a task that extracts sentiment/opinions from text in terms of targets .
Approach:	They propose a meaning-preserving annotation scheme for aspect-based sentiment analysis . they then apply it to two popular ABSA datasets to examine their results .
Outcome:	The proposed approach improves the state of aspect-based sentiment analysis (ABSA) by preserving the meaning of the sentiment.

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Bobo Li, Hao Fei, Fei Li, Yuhan Wu, Jinsong Zhang, Shengqiong Wu, Jingye Li, Yijiang Liu, Lizi Liao, Tat-Seng Chua, Donghong Ji

Uncertainty-Aware Unlikelihood Learning Improves Generative Aspect Sentiment Quad Prediction (2023.findings-acl)

Challenge:	a new task of conversational aspect-based sentiment analysis (DiaASQ) is designed to detect the quadruple of target-aspect-opinion-sentiment in a dialogue.
Approach:	They propose a task of conversational aspect-based sentiment quadruple analysis to detect the quadrangle of target-aspect-opinion-sentiment in a dialogue.
Outcome:	The proposed task is based on a high-quality dataset in Chinese and English . it improves the end-to-end quadruple prediction and integrates rich feature representations .

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Mengting Hu, Yinhao Bai, Yike Wu, Zhen Zhang, Liqi Zhang, Hang Gao, Shiwan Zhao, Minlie Huang

Shoes-ACOSI: A Dataset for Aspect-Based Sentiment Analysis with Implicit Opinion Extraction (2024.findings-emnlp)

Challenge:	Existing studies focus on what to generate but ignore what not to generate . a template-agnostic method boosts original learning and reduces mistakes simultaneously .
Approach:	They propose a template-agnostic method to control the token-level generation . they introduce Monte Carlo dropout to understand the built-in uncertainty of pre-trained language models .
Outcome:	The proposed method boosts original learning and reduces mistakes simultaneously on four public datasets.

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Joseph Peper, Wenzhao Qiu, Ryan Bruggeman, Yi Han, Estefania Chehade, Lu Wang

Error Comparison Optimization for Large Language Models on Aspect-Based Sentiment Analysis (2025.acl-long)

Challenge:	Prior work in ABSA has investigated opinion extraction as an important subtask, but these works only label concise, explicitly-stated opinion spans.
Approach:	They propose a new ABSA dataset with implicit opinion span annotations . they use paragraph-length inputs and prompted-LLM baselines to evaluate the dataset .
Outcome:	The proposed dataset presents significant challenges for fully-supervised models and LLMs.

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Qianlong Wang, Keyang Ding, Hengxin Gao, Hui Wang, Ruifeng Xu

Linking Adaptive Structure Induction and Neuron Filtering: A Spectral Perspective for Aspect-based Sentiment Analysis (2024.lrec-main)

Challenge:	Existing methods for aspect-based sentiment analysis (ABSA) only compare current predictions and labels on each sample, yet fail to perceive and understand its error outputs from different degrees.
Approach:	They propose a framework that can perceive and understand the degree of errors by learning from comparative error pairs.
Outcome:	The proposed framework exceeds baselines and achieves the desired performance.

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Hao Niu, Maoyi Wang, Yun Xiong, Biao Yang, Xing Jia, Zhonglei Guo

Aspect-based Sentiment Analysis via Synthetic Image Generation (2025.findings-emnlp)

Challenge:	incorporating structure information can improve the performance of aspect-based sentiment analysis.
Approach:	They propose a method to conduct neuron-level manipulations on word representations in the frequency domain.
Outcome:	The proposed method can achieve or come close to state-of-the-art in ABSA.

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Ge Chen, Zhongqing Wang, Guodong Zhou