# A Systematic Review of Aspect-based Sentiment Analysis: Domains, Methods, and Trends arXiv: https://arxiv.org/abs/2311.10777 ยท HTML: https://mlsanity.com/paper/2311.10777/ ## Key facts - Cited by 13 papers in the map (data through 14 Oct 2026). - Builds on 25 of its 165 references that are in the map. - First posted on arXiv 16 Nov 2023. ## Builds on (most cited first, 25 of 25) - [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017) - [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018) - [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020) - [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019) - [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019) - [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://mlsanity.com/paper/1910.13461/) (2019) - [Sequence to Sequence Learning with Neural Networks](https://mlsanity.com/paper/1409.3215/) (2014) - [Image-based Recommendations on Styles and Substitutes](https://mlsanity.com/paper/1506.04757/) (2015) - [Ups and Downs: Modeling the Visual Evolution of Fashion Trends with One-Class Collaborative Filtering](https://mlsanity.com/paper/1602.01585/) (2016) - [Aspect Sentiment Quad Prediction as Paraphrase Generation](https://mlsanity.com/paper/2110.00796/) (2021) - [A Survey on Aspect-Based Sentiment Analysis: Tasks, Methods, and Challenges](https://mlsanity.com/paper/2203.01054/) (2022) - [An Interactive Multi-Task Learning Network for End-to-End Aspect-Based Sentiment Analysis](https://mlsanity.com/paper/1906.06906/) (2019) - [SentiHood: Targeted Aspect Based Sentiment Analysis Dataset for Urban Neighbourhoods](https://mlsanity.com/paper/1610.03771/) (2016) - [A Wide Evaluation of ChatGPT on Affective Computing Tasks](https://mlsanity.com/paper/2308.13911/) (2023) - [Tasty Burgers, Soggy Fries: Probing Aspect Robustness in Aspect-Based Sentiment Analysis](https://mlsanity.com/paper/2009.07964/) (2020) - [On the Robustness of Aspect-based Sentiment Analysis: Rethinking Model, Data, and Training](https://mlsanity.com/paper/2304.09563/) (2023) - [A Survey on Aspect-Based Sentiment Classification](https://mlsanity.com/paper/2203.14266/) (2022) - [W2VLDA: Almost Unsupervised System for Aspect Based Sentiment Analysis](https://mlsanity.com/paper/1705.07687/) (2017) - [Over a Decade of Social Opinion Mining: A Systematic Review](https://mlsanity.com/paper/2012.03091/) (2020) - [Cross-Domain Aspect Extraction using Transformers Augmented with Knowledge Graphs](https://mlsanity.com/paper/2210.10144/) (2022) - [SynGen: A Syntactic Plug-and-play Module for Generative Aspect-based Sentiment Analysis](https://mlsanity.com/paper/2302.13032/) (2023) - [Improving Aspect Term Extraction with Bidirectional Dependency Tree Representation](https://mlsanity.com/paper/1805.07889/) (2018) - [Aspect and Opinion Terms Extraction Using Double Embeddings and Attention Mechanism for Indonesian Hotel Reviews](https://mlsanity.com/paper/1908.04899/) (2019) - [Twitter Opinion Topic Model: Extracting Product Opinions from Tweets by Leveraging Hashtags and Sentiment Lexicon](https://mlsanity.com/paper/1609.06578/) (2016) - [Indo LEGO-ABSA: A Multitask Generative Aspect Based Sentiment Analysis for Indonesian Language](https://mlsanity.com/paper/2311.01757/) (2023) ## Built on by (most cited first, 13 of 13) - [EduRABSA: An Education Review Dataset for Aspect-based Sentiment Analysis Tasks](https://mlsanity.com/paper/2508.17008/) (2025) - [SemEval-2026 Task 3: Dimensional Aspect-Based Sentiment Analysis (DimABSA)](https://mlsanity.com/paper/2604.07066/) (2026) - [Multi-Domain ABSA Conversation Dataset Generation via LLMs for Real-World Evaluation and Model Comparison](https://mlsanity.com/paper/2505.24701/) (2025) - [DimABSA: Building Multilingual and Multidomain Datasets for Dimensional Aspect-Based Sentiment Analysis](https://mlsanity.com/paper/2601.23022/) (2026) - [RuOpinionNE-2024: Extraction of Opinion Tuples from Russian News Texts](https://mlsanity.com/paper/2504.06947/) (2025) - [Language of Persuasion and Misrepresentation in Business Communication: A Textual Detection Approach](https://mlsanity.com/paper/2508.09935/) (2025) - [A Retail-Corpus for Aspect-Based Sentiment Analysis with Large Language Models](https://mlsanity.com/paper/2508.17994/) (2025) - [Data-Efficient Adaptation and a Novel Evaluation Method for Aspect-based Sentiment Analysis](https://mlsanity.com/paper/2511.03034/) (2025) - [TeachPro: Multi-Label Qualitative Teaching Evaluation via Cross-View Graph Synergy and Semantic Anchored Evidence Encoding](https://mlsanity.com/paper/2601.09246/) (2026) - [Zero-Shot to Full-Resource: Cross-lingual Transfer Strategies for Aspect-Based Sentiment Analysis](https://mlsanity.com/paper/2604.26619/) (2026) - [Annotation Quality in Aspect-Based Sentiment Analysis: A Case Study Comparing Experts, Students, Crowdworkers, and Large Language Model](https://mlsanity.com/paper/2605.03624/) (2026) - [LLM for the development of FCM](https://mlsanity.com/paper/2607.04983/) (2026) - [Look What You Made Us Cluster: Hate Narrative Extraction from Reddit Discourse](https://mlsanity.com/paper/2609.37408/) (2026) ## Abstract (arXiv) Aspect-based sentiment analysis (ABSA) is a fine-grained type of sentiment analysis that identifies aspects and their associated opinions from a given text. With the surge of digital opinionated text data, ABSA gained increasing popularity for its ability to mine more detailed and targeted insights. Many review papers on ABSA subtasks and solution methodologies exist, however, few focus on trends over time or systemic issues relating to research application domains, datasets, and solution approaches. To fill the gap, this paper presents a systematic literature review (SLR) of ABSA studies with a focus on trends and high-level relationships among these fundamental components. This review is one of the largest SLRs on ABSA. To our knowledge, it is also the first to systematically examine the interrelations among ABSA research and data distribution across domains, as well as trends in solution paradigms and approaches. Our sample includes 727 primary studies screened from 8550 search results without time constraints via an innovative automatic filtering process. Our quantitative analysis not only identifies trends in nearly two decades of ABSA research development but also unveils a systemic lack of dataset and domain diversity as well as domain mismatch that may hinder the development of future ABSA research. We discuss these findings and their implications and propose suggestions for future research.