Challenge: Existing sentiment analysis models treat aspects and targets separately, causing conflicting sentiments.
Approach: They propose an approach that jointly considers aspects and targets when inferring sentiments.
Outcome: The proposed approach outperforms leading models by 1.6% to 4.3% on benchmark datasets . it uses selective attention mechanisms for selective attention between targets and context words .

Similar Papers

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

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

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

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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.
Effective Attention Modeling for Aspect-Level Sentiment Classification (C18-1)

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Challenge: Aspect-level sentiment classification aims to determine sentiment polarity of review sentence towards opinion target . main challenge is to separate different opinion contexts for different targets .
Approach: They propose a method that captures the semantic meaning of the opinion target and a model that incorporates syntactic information into the attention mechanism.
Outcome: The proposed method captures the semantic meaning of the opinion target and incorporates syntactic information into the attention mechanism.
Aspect-to-Scope Oriented Multi-view Contrastive Learning for Aspect-based Sentiment Analysis (2023.findings-emnlp)

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Challenge: Existing methods for Aspect-based sentiment analysis (ABSA) focus on mining syntactic or semantic information, which suffers from noisy interference when multiple aspects exist in a sentence.
Approach: They propose a scope-assisted multi-view graph contrastive learning framework that captures correlation and difference between aspect and syntactic/semantic information.
Outcome: The proposed framework outperforms state-of-the-art methods on five benchmark datasets and verifies its effectiveness and robustness.
OATS: A Challenge Dataset for Opinion Aspect Target Sentiment Joint Detection for Aspect-Based Sentiment Analysis (2024.lrec-main)

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Challenge: Aspect-based sentiment analysis (ABSA) focuses on understanding sentiments specific to distinct elements within a user-generated review.
Approach: They propose to use Aspect-based sentiment analysis to understand specific aspects of a user-generated review to identify the target entity being reviewed, the aspect to which it belongs, the opinion phrase, and the sentiment expressed toward the aspects.
Outcome: The proposed dataset bridges the gaps observed in existing datasets and sheds light on various ABSA subtasks.
Enhancing Aspect-level Sentiment Analysis with Word Dependencies (2021.eacl-main)

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Challenge: Existing approaches to enhance aspect-level sentiment analysis have omitted syntactic information . experimental results show that our approach outperforms baseline models on all datasets .
Approach: They propose to leverage word dependencies to enhance aspect-level sentiment analysis . they propose to use key-value memory networks to leverage different dependency results .
Outcome: The proposed approach outperforms baseline models on all datasets and achieves state-of-the-art performance on three of them.
Dynamic Multi-granularity Attribution Network for Aspect-based Sentiment Analysis (2024.emnlp-main)

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Challenge: Existing methods for predicting sentiment polarity of aspects are susceptible to interference caused by irrelevant contexts and lack sentiment knowledge at a data-specific level.
Approach: They propose a novel Aspect-based sentiment analysis method that leverages attention scores to model the relationships between aspects and contexts.
Outcome: The proposed method is able to predict sentiments from a set of five benchmark datasets.
AoM: Detecting Aspect-oriented Information for Multimodal Aspect-Based Sentiment Analysis (2023.findings-acl)

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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.
Regrexit or not Regrexit: Aspect-based Sentiment Analysis in Polarized Contexts (2020.coling-main)

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Challenge: Aspect-based Sentiment Analysis (ABSA) aims at capturing sentiment expressed toward each aspect of a target entity.
Approach: They propose to extend the task of Aspect-based Sentiment Analysis (ABSA) toward affect and emotion representation in polarized settings.
Outcome: The proposed model captures aspect-based polarization from newspapers regarding the Brexit scenario of 1.2m entities at sentence-level.

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