Aspect-Based Sentiment Analysis (ABSA)
166 papers with code • 18 benchmarks • 18 datasets
Aspect-Based Sentiment Analysis (ABSA) is a Natural Language Processing task that aims to identify and extract the sentiment of specific aspects or components of a product or service. ABSA typically involves a multi-step process that begins with identifying the aspects or features of the product or service that are being discussed in the text. This is followed by sentiment analysis, where the sentiment polarity (positive, negative, or neutral) is assigned to each aspect based on the context of the sentence or document. Finally, the results are aggregated to provide an overall sentiment for each aspect.
And recent works propose more challenging ABSA tasks to predict sentiment triplets or quadruplets (Chen et al., 2022), the most influential of which are ASTE (Peng et al., 2020; Zhai et al., 2022), TASD (Wan et al., 2020), ASQP (Zhang et al., 2021a) and ACOS with an emphasis on the implicit aspects or opinions (Cai et al., 2020a).
( Source: MvP: Multi-view Prompting Improves Aspect Sentiment Tuple Prediction )
Benchmarks
These leaderboards are used to track progress in Aspect-Based Sentiment Analysis (ABSA)
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Libraries
Use these libraries to find Aspect-Based Sentiment Analysis (ABSA) models and implementationsDatasets
SemEval-2014 Task-4
MAMS
Twitter Sentiment Analysis
Subtasks
Latest papers with no code
Entity-Aspect-Opinion-Sentiment Quadruple Extraction for Fine-grained Sentiment Analysis
To facilitate research in this new task, we have constructed four datasets (Res14-EASQE, Res15-EASQE, Res16-EASQE, and Lap14-EASQE) based on the SemEval Restaurant and Laptop datasets.
Syntax-Informed Interactive Model for Comprehensive Aspect-Based Sentiment Analysis
Aspect-based sentiment analysis (ABSA), a nuanced task in text analysis, seeks to discern sentiment orientation linked to specific aspect terms in text.
A Systematic Review of Aspect-based Sentiment Analysis (ABSA): Domains, Methods, and Trends
This review is one of the largest SLRs on ABSA, and also, to our knowledge, the first that systematically examines the trends and inter-relations among ABSA research and data distribution across domains and solution paradigms and approaches.
iACOS: Advancing Implicit Sentiment Extraction with Informative and Adaptive Negative Examples
Aspect-based sentiment analysis (ABSA) have been extensively studied, but little light has been shed on the quadruple extraction consisting of four fundamental elements: aspects, categories, opinions and sentiments, especially with implicit aspects and opinions.
Document-Level Supervision for Multi-Aspect Sentiment Analysis Without Fine-grained Labels
Aspect-based sentiment analysis (ABSA) is a widely studied topic, most often trained through supervision from human annotations of opinionated texts.
Towards Robust Aspect-based Sentiment Analysis through Non-counterfactual Augmentations
While state-of-the-art NLP models have demonstrated excellent performance for aspect based sentiment analysis (ABSA), substantial evidence has been presented on their lack of robustness.
A Novel Counterfactual Data Augmentation Method for Aspect-Based Sentiment Analysis
To mitigate this problem, we propose a novel and simple counterfactual data augmentation method to generate opinion expressions with reversed sentiment polarity.
Domain-Expanded ASTE: Rethinking Generalization in Aspect Sentiment Triplet Extraction
Aspect Sentiment Triplet Extraction (ASTE) is a subtask of Aspect-Based Sentiment Analysis (ABSA) that considers each opinion term, their expressed sentiment, and the corresponding aspect targets.
A Weak Supervision Approach for Few-Shot Aspect Based Sentiment
We explore how weak supervision on abundant unlabeled data can be leveraged to improve few-shot performance in aspect-based sentiment analysis (ABSA) tasks.
On the Robustness of Aspect-based Sentiment Analysis: Rethinking Model, Data, and Training
In this study, we propose to enhance the ABSA robustness by systematically rethinking the bottlenecks from all possible angles, including model, data, and training.
