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
Double Embeddings and CNN-based Sequence Labeling for Aspect Extraction
Unlike other highly sophisticated supervised deep learning models, this paper proposes a novel and yet simple CNN model employing two types of pre-trained embeddings for aspect extraction: general-purpose embeddings and domain-specific embeddings.
An Unsupervised Approach for Aspect Category Detection Using Soft Cosine Similarity Measure
Besides, most of these supervised methods require feature engineering to perform well.
An Iterative Multi-Knowledge Transfer Network for Aspect-Based Sentiment Analysis
XL2248/IKTN
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Findings (EMNLP) 2021
Aspect-based sentiment analysis (ABSA) mainly involves three subtasks: aspect term extraction, opinion term extraction, and aspect-level sentiment classification, which are typically handled in a separate or joint manner.
A Hybrid Approach for Aspect-Based Sentiment Analysis Using Deep Contextual Word Embeddings and Hierarchical Attention
In this paper we extend the state-of-the-art Hybrid Approach for Aspect-Based Sentiment Analysis (HAABSA) method in two directions.
Enhancing Fine-grained Sentiment Classification Exploiting Local Context Embedding
Target-oriented sentiment classification is a fine-grained task of natural language processing to analyze the sentiment polarity of the targets.
Improving BERT Performance for Aspect-Based Sentiment Analysis
IMPLabUniPr/BERT-for-ABSA
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ICNLSP 2021
Aspect-Based Sentiment Analysis (ABSA) studies the consumer opinion on the market products.
Understanding Pre-trained BERT for Aspect-based Sentiment Analysis
howardhsu/BERT-for-RRC-ABSA
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COLING 2020
Most features in the representation of an aspect are dedicated to the fine-grained semantics of the domain (or product category) and the aspect itself, instead of carrying summarized opinions from its context.
Aspect-based Sentiment Analysis with Type-aware Graph Convolutional Networks and Layer Ensemble
cuhksz-nlp/ASA-TGCN
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NAACL 2021
It is popular that neural graph-based models are applied in existing aspect-based sentiment analysis (ABSA) studies for utilizing word relations through dependency parses to facilitate the task with better semantic guidance for analyzing context and aspect words.
AR-BERT: Aspect-relation enhanced Aspect-level Sentiment Classification with Multi-modal Explanations
We propose AR-BERT, a novel two-level global-local entity embedding scheme that allows efficient joint training of KG-based aspect embeddings and ALSC models.
A Deep Convolutional Neural Networks Based Multi-Task Ensemble Model for Aspect and Polarity Classification in Persian Reviews
The results indicate that this new approach increases the efficiency of the sentiment analysis model in the Persian language.
