| Challenge: | Existing methods to extract aspects from opinions focus on explicit aspects, but sentences do not state them explicitly. |
| Approach: | They propose to use a dictionary-based approach to identify and extract aspects from opinions . they propose to combine topic modelling and dictionary--based method . |
| Outcome: | The proposed models outperform baseline topic model and dictionary-based approach in 58.70% of the evaluations. |
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| 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. |
Aspect-Category-Opinion-Sentiment Quadruple Extraction with Implicit Aspects and Opinions (2021.acl-long)
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| 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 . |
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From Annotation to Adaptation: Metrics, Synthetic Data, and Aspect Extraction for Aspect-Based Sentiment Analysis with Large Language Models (2025.naacl-srw)
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| Challenge: | Using a synthetic sports feedback dataset, we evaluate open-weight LLMs’ ability to extract aspect-polarity pairs. |
| Approach: | They propose a metric to facilitate the evaluation of aspect extraction with generative models. |
| Outcome: | The proposed metric improves the performance of open-weight LLMs in the Aspect-Based Sentiment Analysis task. |
Shoes-ACOSI: A Dataset for Aspect-Based Sentiment Analysis with Implicit Opinion Extraction (2024.findings-emnlp)
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| 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. |
Progressive Self-Training with Discriminator for Aspect Term Extraction (2021.emnlp-main)
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| Challenge: | Existing approaches to extract aspect terms from review sentences are limited due to lack of annotated data. |
| Approach: | They propose to refine conventional self-training to progressive self-teaching to reduce noise . they use a discriminator to filter the noisy pseudo-labels. |
| Outcome: | The proposed model outperforms baseline models and achieves state-of-the-art performance on four SemEval datasets. |
Double Embeddings and CNN-based Sequence Labeling for Aspect Extraction (P18-2)
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| Challenge: | Recent supervised deep learning models have achieved state-of-the-art performance, but there are two other considerations that are important. |
| Approach: | They propose a supervised aspect extraction model using general-purpose embeddings and domain-specific embeddables. |
| Outcome: | The proposed model outperforms state-of-the-art methods without supervision and achieves very good results. |
Aspect Extraction Using Coreference Resolution and Unsupervised Filtering (2020.aacl-srw)
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| 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. |
Aspect-aware Unsupervised Extractive Opinion Summarization (2023.findings-acl)
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| Challenge: | Extractive opinion summarization extracts sentences from reviews to represent the prevalent opinions about a product or service. |
| Approach: | They propose a method for unsupervised extractive opinion summarization that automatically identifies the aspects described in review sentences and extracts sentences based on their aspects. |
| Outcome: | The proposed method improves aspect coverage and performs well on multiple opinion summarization datasets. |
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. |
Embarrassingly Simple Unsupervised Aspect Extraction (2020.acl-main)
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| Challenge: | Existing systems for aspect extraction are supervised, but are unlikely to transfer well between domains. |
| Approach: | They propose a novel approach that uses an RBF kernel to generate a single-head attention mechanism for aspect extraction from text. |
| Outcome: | The proposed method is based on an RBF kernel and can be applied to new domains and languages. |