Challenge: Existing research efforts focus on targeting sentiment analysis as a sequence labeling problem, building models that can capture explicit structures in the output space.
Approach: They argue that both implicit and explicit structural information are crucial for building a successful targeted sentiment analysis model.
Outcome: The proposed model outperforms existing models by capturing implicit and explicit structural information.

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Open-Domain Targeted Sentiment Analysis via Span-Based Extraction and Classification (P19-1)

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Challenge: Existing approaches to target sentiment analysis are limited by huge search space and sentiment inconsistency.
Approach: They propose a span-based extract-then-classify framework to detect opinion targets . they propose pipeline, joint, and collapsed models to classify polarities .
Outcome: The proposed framework outperforms the sequence tagging baseline on three benchmark datasets.
Paraphrase Makes Perfect: Leveraging Expression Paraphrase to Improve Implicit Sentiment Learning (2025.coling-main)

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Challenge: Existing implicit sentiment learning methods focus on capturing implicit sentiment knowledge individually, without considering the potential connection between implicit and explicit sentiment.
Approach: They propose an expression paraphrase strategy and a sentiment-consistent contrastive learning mechanism to learn the connections between implicit and explicit sentiment expressions and integrate them into the model.
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A Sequence-to-Structure Approach to Document-level Targeted Sentiment Analysis (2023.findings-emnlp)

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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.
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Implicit Sentiment Analysis with Event-centered Text Representation (2021.emnlp-main)

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Challenge: Existing methods for implicit sentiment analysis simply view noun phrases or entities in text as events or indirectly model events with sophisticated models.
Approach: They propose an event-centric implicit sentiment analysis that utilizes the sentiment-aware event contained in a sentence to infer sentiment polarity.
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Targeted Distillation for Sentiment Analysis (2025.emnlp-main)

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Challenge: Recent studies demonstrate that large language models exhibit remarkable capabilities and achieve state-of-the-art performance in diverse sentiment analysis tasks.
Approach: They propose a distillation framework that decouples knowledge from alignment and introduces a sentiment analysis benchmark that covers a diverse set of tasks.
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Structured Sentiment Analysis as Dependency Graph Parsing (2021.acl-long)

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Challenge: Structured sentiment analysis attempts to extract full opinion tuples from a text, but has been subdivided into smaller and smaller sub-tasks, e.g., target extraction or targeted polarity classification.
Approach: They propose a framework which jointly predicts all elements of an opinion tuple and their relations by using dependency graph parsing.
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Cross-Domain Sentiment Classification with Target Domain Specific Information (P18-1)

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Challenge: Existing methods for sentiment classification focus on learning domain-invariant representations . few of them pay attention to domain-specific information, which should also be informative.
Approach: They propose a method to extract domain specific and invariant representations and train a classifier on each of them.
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KC-ISA: An Implicit Sentiment Analysis Model Combining Knowledge Enhancement and Context Features (2022.coling-1)

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Challenge: Existing research results on explicit sentiment analysis are limited . implicit sentiment analysis is a process of analyzing text based on whether it contains explicit sentiment words.
Approach: They propose a model that integrates external knowledge and contextual features . they use a knowledge graph to supplement implicit sentiment expression .
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Semantic Simplification for Sentiment Classification (2022.emnlp-main)

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Challenge: Recent work on document-level sentiment classification has shown that the sentiment in the original text is often hard to capture . previous studies focus on predicting the overall sentiment from original text using statistical or neural models, but these methods either heavily rely on human knowledge or suffer from the complex structure of the text.
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Learning Implicit Sentiment in Aspect-based Sentiment Analysis with Supervised Contrastive Pre-Training (2021.emnlp-main)

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Challenge: Recent studies have focused on identifying the sentiment polarity of aspects in product reviews.
Approach: They propose to use supervised Contrastive Pre-Training to learn implicit sentiment . they propose to train large-scale sentiment-annotated corpora from in-domain language resources .
Outcome: The proposed model achieves state-of-the-art performance on SemEval2014 benchmarks and comprehensively validates its effectiveness on learning implicit sentiment.

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