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
Most implemented papers
Recurrent Attention Network on Memory for Aspect Sentiment Analysis
songyouwei/ABSA-PyTorch
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EMNLP 2017
We propose a novel framework based on neural networks to identify the sentiment of opinion targets in a comment/review.
An Interactive Multi-Task Learning Network for End-to-End Aspect-Based Sentiment Analysis
Aspect-based sentiment analysis produces a list of aspect terms and their corresponding sentiments for a natural language sentence.
Adapt or Get Left Behind: Domain Adaptation through BERT Language Model Finetuning for Aspect-Target Sentiment Classification
deepopinion/domain-adapted-atsc
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LREC 2020
Aspect-Target Sentiment Classification (ATSC) is a subtask of Aspect-Based Sentiment Analysis (ABSA), which has many applications e. g. in e-commerce, where data and insights from reviews can be leveraged to create value for businesses and customers.
Generalizing Natural Language Analysis through Span-relation Representations
Natural language processing covers a wide variety of tasks predicting syntax, semantics, and information content, and usually each type of output is generated with specially designed architectures.
Toward Tag-free Aspect Based Sentiment Analysis: A Multiple Attention Network Approach
qiangyao1988/Toward-tag-free-ABSA
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Existing aspect based sentiment analysis (ABSA) approaches leverage various neural network models to extract the aspect sentiments via learning aspect-specific feature representations.
A Dependency Syntactic Knowledge Augmented Interactive Architecture for End-to-End Aspect-based Sentiment Analysis
XL2248/DREGCN
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The aspect-based sentiment analysis (ABSA) task remains to be a long-standing challenge, which aims to extract the aspect term and then identify its sentiment orientation. In previous approaches, the explicit syntactic structure of a sentence, which reflects the syntax properties of natural language and hence is intuitively crucial for aspect term extraction and sentiment recognition, is typically neglected or insufficiently modeled.
A Multi-task Learning Framework for Opinion Triplet Extraction
GeneZC/OTE-MTL
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Findings of the Association for Computational Linguistics 2020
The state-of-the-art Aspect-based Sentiment Analysis (ABSA) approaches are mainly based on either detecting aspect terms and their corresponding sentiment polarities, or co-extracting aspect and opinion terms.
A Unified Generative Framework for Aspect-Based Sentiment Analysis
Aspect-based Sentiment Analysis (ABSA) aims to identify the aspect terms, their corresponding sentiment polarities, and the opinion terms.
Aspect Level Sentiment Classification with Attention-over-Attention Neural Networks
Aspect Based Sentiment Analysis, PyTorch Implementations.
Transformation Networks for Target-Oriented Sentiment Classification
Between the two layers, we propose a component to generate target-specific representations of words in the sentence, meanwhile incorporate a mechanism for preserving the original contextual information from the RNN layer.
