SOTAVerified

Aspect Extraction

Aspect extraction is the task of identifying and extracting terms relevant for opinion mining and sentiment analysis, for example terms for product attributes or features.

Papers

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Showing 1–25 of 92 papers

TitleStatusHype
PL-FGSA: A Prompt Learning Framework for Fine-Grained Sentiment Analysis Based on MindSpore—0
From Annotation to Adaptation: Metrics, Synthetic Data, and Aspect Extraction for Aspect-Based Sentiment Analysis with Large Language Models—0
An Aspect Extraction Framework using Different Embedding Types, Learning Models, and Dependency StructureCode0
Visual Zero-Shot E-Commerce Product Attribute Value Extraction—0
Learning to Extract Cross-Domain Aspects and Understanding Sentiments Using Large Language Models—0
Instruct-DeBERTa: A Hybrid Approach for Aspect-based Sentiment Analysis on Textual Reviews—0
SEOpinion: Summarization and Exploration Opinion of E-Commerce Websites—0
Deep Learning Brasil at ABSAPT 2022: Portuguese Transformer Ensemble ApproachesCode0
Automatic Aspect Extraction from Scientific TextsCode0
A Wide Evaluation of ChatGPT on Affective Computing TasksCode0
Prompt Tuning Large Language Models on Personalized Aspect Extraction for Recommendations—0
A Weak Supervision Approach for Few-Shot Aspect Based Sentiment—0
Self-training through Classifier Disagreement for Cross-Domain Opinion Target Extraction—0
InstructABSA: Instruction Learning for Aspect Based Sentiment AnalysisCode1
A Framework of Customer Review Analysis Using the Aspect-Based Opinion Mining Approach—0
UnifiedABSA: A Unified ABSA Framework Based on Multi-task Instruction Tuning—0
Cross-Domain Aspect Extraction using Transformers Augmented with Knowledge GraphsCode4
Ensemble Creation via Anchored Regularization for Unsupervised Aspect Extraction—0
A Hierarchical Interactive Network for Joint Span-based Aspect-Sentiment AnalysisCode0
SETSum: Summarization and Visualization of Student Evaluations of TeachingCode0
Distantly Supervised Aspect Clustering And Naming For E-Commerce Reviews—0
Automatic Controllable Product Copywriting for E-CommerceCode1
Dataset and Baseline for Automatic Student Feedback Analysis—0
Zero-Shot Aspect-Based Sentiment Analysis—0
Hyperbolic Disentangled Representation for Fine-Grained Aspect ExtractionCode0
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All datasetsSemEval-2014 Task-4SemEval 2015 Task 12SemEval 2014 Task 4 Sub Task 1SemEval 2016 Task 5 Sub Task 1 Slot 2YASO - YELP

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1InstructABSALaptop (F1)92.3—Unverified
2ACELaptop (F1)87.4—Unverified
3PH-SUMLaptop (F1)86.09—Unverified
4BATLaptop (F1)85.57—Unverified
5DE-CNNRestaurant (F1)85.2—Unverified
6Wei et al. (2020)Laptop (F1)82.7—Unverified
#ModelMetricClaimedVerifiedStatus
1ACERestaurant (F1)80.3—Unverified
2Wei et al. (2020)Restaurant (F1)72.7—Unverified
3DE-CNNRestaurant (F1)68.28—Unverified
#ModelMetricClaimedVerifiedStatus
1DE-CNNLaptop (F1)81.59—Unverified
#ModelMetricClaimedVerifiedStatus
1DE-CNNRestaurant (F1)74.37—Unverified
#ModelMetricClaimedVerifiedStatus
1RACL - LaptopsF123—Unverified