Challenge: Aspect-based sentiment analysis (ABSA) predicts sentiment polarity towards a specific aspect in a sentence.
Approach: They propose to use a dynamic aspect-oriented semantics-based method to learn ABSA.
Outcome: The proposed method can learn dynamic aspect-oriented semantics for ABSA on three benchmark datasets.

Similar Papers

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

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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.
Exploiting BERT for End-to-End Aspect-based Sentiment Analysis (D19-55)

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Challenge: Existing studies on ABSA use a sequence tagging problem to extract aspect-specific opinion words from the sentence given the aspect.
Approach: They build a series of simple yet insightful neural baselines to deal with E2E-ABSA task using contextualized embeddings from pre-trained language models.
Outcome: The proposed architecture outperforms state-of-the-art models even with a simple linear classification layer.
Utilizing BERT for Aspect-Based Sentiment Analysis via Constructing Auxiliary Sentence (N19-1)

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Challenge: Sentiment analysis (SA) is a computational task that aims to identify opinion polarity towards a specific aspect.
Approach: They propose to convert ABSA into a sentence-pair classification task such as question answering and natural language inference.
Outcome: The proposed model is fine-tuned and achieves state-of-the-art on SentiHood and SemEval-2014 datasets.
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.
BERT4GCN: Using BERT Intermediate Layers to Augment GCN for Aspect-based Sentiment Classification (2021.emnlp-main)

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Challenge: Existing approaches to Aspect-based sentiment classification ignore sequential features of context and lack syntactic knowledge of sentences.
Approach: They propose a model which integrates sequential grammatical features from context and syntactic knowledge from dependency graphs to augment GCN to better encode dependency graph outputs.
Outcome: The proposed model outperforms state-of-the-art models when equipped with contextual word embedding from pre-training language models.
Adapt or Get Left Behind: Domain Adaptation through BERT Language Model Finetuning for Aspect-Target Sentiment Classification (2020.lrec-1)

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Challenge: Aspect-Target Sentiment Classification (ATSC) is a subtask of Aspect Based Sentimence Analysis (ABSA) . recent deep transfer-learning methods have been applied successfully to a myriad of NLP tasks.
Approach: They propose to use a self-supervised domain-specific BERT language model to exploit ATSC . they also perform cross-domain evaluation to explore the real-world robustness of their models .
Outcome: The proposed model outperforms baseline models on the SemEval 2014 task 4 restaurants dataset.
Syntactically Aware Cross-Domain Aspect and Opinion Terms Extraction (2020.coling-main)

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Challenge: Supervised-learning approaches fail to scale across domains where labeled data is lacking.
Approach: They propose a method for incorporating external linguistic knowledge into a self-attention mechanism coupled with a transformer-based model.
Outcome: The proposed method enables leveraging syntactic knowledge from transformer-based models to bridge the gap between domains.
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.
Syntax-Aware Graph Attention Network for Aspect-Level Sentiment Classification (2020.coling-main)

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Challenge: Existing approaches to aspect-level sentiment classification focus on modeling the relationship between aspect words and their contexts with attention, and ignore the use of elaborate knowledge implicit in the context.
Approach: They exploit syntactic awareness to the model by the graph attention network on the dependency tree structure and external pre-training knowledge by BERT language model, which helps to model the interaction between the context and aspect words better.
Outcome: The proposed model can model the interaction between the context and aspect words better by using syntactic awareness and external pre-training knowledge.
Towards Unifying the Label Space for Aspect- and Sentence-based Sentiment Analysis (2022.findings-acl)

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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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