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
FaiMA: Feature-aware In-context Learning for Multi-domain Aspect-based Sentiment Analysis
While existing research narrowly focuses on single-domain applications constrained by methodological limitations and data scarcity, the reality is that sentiment naturally traverses multiple domains.
DINER: Debiasing Aspect-based Sentiment Analysis with Multi-variable Causal Inference
However, most of the present debiasing methods focus on single-variable causal inference, which is not suitable for ABSA with two input variables (the target aspect and the review).
Let's Rectify Step by Step: Improving Aspect-based Sentiment Analysis with Diffusion Models
Aspect-Based Sentiment Analysis (ABSA) stands as a crucial task in predicting the sentiment polarity associated with identified aspects within text.
Extensible Multi-Granularity Fusion Network for Aspect-based Sentiment Analysis
TYZY89/EMGF
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This paper presents the Extensible Multi-Granularity Fusion (EMGF) network, which integrates information from dependency and constituent syntactic, attention semantic , and external knowledge graphs.
FABSA: An aspect-based sentiment analysis dataset of user reviews
The majority of available ABSA systems heavily rely on manually annotated datasets to train supervised machine learning models.
RDGCN: Reinforced Dependency Graph Convolutional Network for Aspect-based Sentiment Analysis
rdgcn/rdgcn
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Aspect-based sentiment analysis (ABSA) is dedicated to forecasting the sentiment polarity of aspect terms within sentences.
Deep Learning Brasil at ABSAPT 2022: Portuguese Transformer Ensemble Approaches
ju-resplande/dlb_absapt2022
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Aspect-based Sentiment Analysis (ABSA) is a task whose objective is to classify the individual sentiment polarity of all entities, called aspects, in a sentence.
Large language models for aspect-based sentiment analysis
We assess the performance of GPT-4 and GPT-3. 5 in zero shot, few shot and fine-tuned settings on the aspect-based sentiment analysis (ABSA) task.
OATS: Opinion Aspect Target Sentiment Quadruple Extraction Dataset for Aspect-Based Sentiment Analysis
Aspect-based sentiment analysis (ABSA) delves into understanding sentiments specific to distinct elements within a user-generated review.
UniSA: Unified Generative Framework for Sentiment Analysis
dawn0815/UniSA
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Sentiment analysis is a crucial task that aims to understand people's emotional states and predict emotional categories based on multimodal information.
