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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Multi-level Association Refinement Network for Dialogue Aspect-based Sentiment Quadruple Analysis (2025.acl-long)

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Challenge: Existing methods for identifying quadruples rely on predefined dialogue structure and word semantics to achieve accurate and comprehensive sentiment associations between utterances and words.
Approach: They propose a multi-level association refinement network to achieve more accurate sentiment associations between utterances and words.
Outcome: The proposed framework achieves state-of-the-art performance under low-resource conditions.
DMIN: A Discourse-specific Multi-granularity Integration Network for Conversational Aspect-based Sentiment Quadruple Analysis (2024.findings-acl)

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Challenge: Existing studies focus on enhancing token-level interactions, but lack sufficient modeling of discourse structure information.
Approach: They propose to use a discourse structure called "thread" to enhance token interaction among different utterances.
Outcome: The proposed model achieves state-of-the-art on two datasets.
Inter-sentence Context Modeling and Structure-aware Representation Enhancement for Conversational Sentiment Quadruple Extraction (2025.emnlp-main)

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Challenge: Existing studies struggle to capture complete dialogue semantics due to inadequate inter-utterance modeling and the underutilization of dialogue structure.
Approach: They propose a model to extract dialogue aspect sentiment quadruples from dialogues using a sentence-by-sentence encoding module.
Outcome: The proposed model extracts quadruples of target-aspect-opinion-sentiment from dialogues.
Aspect-based Sentiment Analysis in Question Answering Forums (2021.findings-emnlp)

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Challenge: Existing studies on aspects-based sentiment analysis focus on a single opinionated sentence.
Approach: They propose a model to combine aspects and their sentiments for QA forums . they use cross-sentence aspect-opinion interaction modeling to align the aspect mentioned in the question and associated opinion clues in the answer.
Outcome: The proposed model outperforms baseline models on three real-world datasets.
Is Compound Aspect-Based Sentiment Analysis Addressed by LLMs? (2024.findings-emnlp)

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Challenge: Aspect-based sentiment analysis (ABSA) aims to predict aspect-based elements from text . large language models (LLMs) have impressive abilities in handling human instructions .
Approach: They propose a framework to evaluate LLMs' ability to handle complex ABSA tasks . they use constrained prompts to automatically organize the returned predictions .
Outcome: The proposed framework outperforms supervised methods in some cases, but it is still lacking in other areas.
Task-aware Contrastive Mixture of Experts for Quadruple Extraction in Conversations with Code-like Replies and Non-opinion Detection (2025.emnlp-main)

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Challenge: Applying Large Language Models (LLMs) for this specific task presents two primary challenges: the accurate extraction of multiple elements and the understanding of complex dialogue reply structure.
Approach: They propose a novel LLM-based multi-task approach to extract sentiment quadruples from conversations by integrating expert-level contrastive loss within task-oriented mixture of experts layer.
Outcome: The proposed method outperforms existing fine-tuning techniques in terms of accuracy and computational efficiency.
Benchmark Creation for Aspect-Based Sentiment Analysis in Low-Resource Odia Language and Evaluation through Fine-Tuning of Multilingual Models (2025.coling-main)

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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 .
M-ABSA: A Multilingual Dataset for Aspect-Based Sentiment Analysis (2025.emnlp-main)

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Challenge: Existing studies focus on English-centric aspects of sentiment analysis, limiting scope for multilingual evaluation and research.
Approach: They propose to use a multilingual dataset to analyze aspects with associated sentiment elements in text.
Outcome: The proposed dataset is the most extensive multilingual parallel dataset for ABSA to date.
E-ABSA20K: A Dataset and Propose-and-Verify for Aspect-Based Sentiment Analysis in Long E-commerce Reviews (2026.findings-acl)

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Challenge: Aspect-based Sentiment Analysis (ABSA) is critical for extracting actionable product insights from e-commerce reviews.
Approach: They propose a framework that decomposes ABSA into two stages to extract review-level quadruple reviews from 20K reviews from four product categories.
Outcome: The proposed framework outperforms existing benchmarks and single-stage prompting and competitive ABSA extraction baselines.
Towards Generative Aspect-Based Sentiment Analysis (2021.acl-short)

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Challenge: Existing work on Aspect-based sentiment analysis ignores the rich label semantics of ABSA.
Approach: They propose to tackle various ABSA tasks in a unified generative framework . they propose to use annotation-style and extraction-style modeling to enable training .
Outcome: The proposed framework achieves state-of-the-art on four ABSA tasks across multiple benchmark datasets.

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