mlsanityData to 7 Oct 2026

A Survey on Aspect-Based Sentiment Classification

arXiv 2203.14266 ↗First posted 27 Mar 2022cs.CLcited by 18

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Abstract (arXiv)

With the constantly growing number of reviews and other sentiment-bearing texts on the Web, the demand for automatic sentiment analysis algorithms continues to expand. Aspect-based sentiment classification (ABSC) allows for the automatic extraction of highly fine-grained sentiment information from text documents or sentences. In this survey, the rapidly evolving state of the research on ABSC is reviewed. A novel taxonomy is proposed that categorizes the ABSC models into three major categories: knowledge-based, machine learning, and hybrid models. This taxonomy is accompanied with summarizing overviews of the reported model performances, and both technical and intuitive explanations of the various ABSC models. State-of-the-art ABSC models are discussed, such as models based on the transformer model, and hybrid deep learning models that incorporate knowledge bases. Additionally, various techniques for representing the model inputs and evaluating the model outputs are reviewed. Furthermore, trends in the research on ABSC are identified and a discussion is provided on the ways in which the field of ABSC can be advanced in the future.

Key facts. Cited by 18 papers in the map (data through 7 Oct 2026). Builds on 49 of its 223 references that are in the map. First posted on arXiv 27 Mar 2022.

Builds on

49 of 223 references.

References · 49

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Citing papers per year: 2022: 2; 2023: 3; 2024: 5; 2025: 8.

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  1. 2025GRAM: A Generative Foundation Reward Model for Reward Generalizationcited by 25
  2. 2023A Systematic Review of Aspect-based Sentiment Analysis: Domains, Methods, and Trendscited by 13
  3. 2023Unlocking the Emotional World of Visual Media: An Overview of the Science, Research, and Impact of Understanding Emotioncited by 12
  4. 2023Uncertainty-Aware Unlikelihood Learning Improves Generative Aspect Sentiment Quad Predictioncited by 10
  5. 2025From LLM-anation to LLM-orchestrator: Coordinating Small Models for Data Labelingcited by 8
  6. 2022Experiencer-Specific Emotion and Appraisal Predictioncited by 4
  7. 2022Survey of Aspect-based Sentiment Analysis Datasetscited by 3
  8. 2025Large Language Models Enhanced by Plug and Play Syntactic Knowledge for Aspect-based Sentiment Analysiscited by 3
  9. 2024Dancing in the syntax forest: fast, accurate and explainable sentiment analysis with SALSAcited by 2
  10. 2024Fine-tuning multilingual language models in Twitter/X sentiment analysis: a study on Eastern-European V4 languagescited by 2
  11. 2025From Argumentation to Deliberation: Perspectivized Stance Vectors for Fine-grained (Dis)agreement Analysiscited by 2
  12. 2024Enhancing Aspect-based Sentiment Analysis in Tourism Using Large Language Models and Positional Informationcited by 1
6 more
  1. 2025From Annotation to Adaptation: Metrics, Synthetic Data, and Aspect Extraction for Aspect-Based Sentiment Analysis with Large Language Modelscited by 1
  2. 2025Cross-lingual Aspect-Based Sentiment Analysis: A Survey on Tasks, Approaches, and Challengescited by 1
  3. 2024Linguistic features for sentence difficulty prediction in ABSAcited by 0
  4. 2024Target-Dependent Multimodal Sentiment Analysis Via Employing Visual-to Emotional-Caption Translation Network using Visual-Caption Pairscited by 0
  5. 2025PL-FGSA: A Prompt Learning Framework for Fine-Grained Sentiment Analysis Based on MindSporecited by 0
  6. 2025EmoBench-UA: A Benchmark Dataset for Emotion Detection in Ukrainiancited by 0