# InstructABSA: Instruction Learning for Aspect Based Sentiment Analysis

arXiv: https://arxiv.org/abs/2302.08624 · HTML: https://mlsanity.com/paper/2302.08624/

## Key facts

- Cited by 23 papers in the map (data through 14 Oct 2026).
- Builds on 53 of its 89 references that are in the map.
- First posted on arXiv 16 Feb 2023.

## Builds on (most cited first, 30 of 53)

- [Training language models to follow instructions with human feedback](https://mlsanity.com/paper/2203.02155/) (2022)
- [LoRA: Low-Rank Adaptation of Large Language Models](https://mlsanity.com/paper/2106.09685/) (2021)
- [Finetuned Language Models Are Zero-Shot Learners](https://mlsanity.com/paper/2109.01652/) (2021)
- [Self-Instruct: Aligning Language Models with Self-Generated Instructions](https://mlsanity.com/paper/2212.10560/) (2022)
- [DeBERTa: Decoding-enhanced BERT with Disentangled Attention](https://mlsanity.com/paper/2006.03654/) (2020)
- [Learn to Explain: Multimodal Reasoning via Thought Chains for Science Question Answering](https://mlsanity.com/paper/2209.09513/) (2022)
- [Multitask Prompted Training Enables Zero-Shot Task Generalization](https://mlsanity.com/paper/2110.08207/) (2021)
- [CodeBERT: A Pre-Trained Model for Programming and Natural Languages](https://mlsanity.com/paper/2002.08155/) (2020)
- [Super-NaturalInstructions: Generalization via Declarative Instructions on 1600+ NLP Tasks](https://mlsanity.com/paper/2204.07705/) (2022)
- [Cross-Task Generalization via Natural Language Crowdsourcing Instructions](https://mlsanity.com/paper/2104.08773/) (2021)
- [CodeSearchNet Challenge: Evaluating the State of Semantic Code Search](https://mlsanity.com/paper/1909.09436/) (2019)
- [MetaICL: Learning to Learn In Context](https://mlsanity.com/paper/2110.15943/) (2021)
- [AI Chains: Transparent and Controllable Human-AI Interaction by Chaining Large Language Model Prompts](https://mlsanity.com/paper/2110.01691/) (2021)
- [code2vec: Learning Distributed Representations of Code](https://mlsanity.com/paper/1803.09473/) (2018)
- [Learning to Represent Programs with Graphs](https://mlsanity.com/paper/1711.00740/) (2017)
- [Reframing Instructional Prompts to GPTk's Language](https://mlsanity.com/paper/2109.07830/) (2021)
- [DeepCoder: Learning to Write Programs](https://mlsanity.com/paper/1611.01989/) (2016)
- [Adapting Language Models for Zero-shot Learning by Meta-tuning on Dataset and Prompt Collections](https://mlsanity.com/paper/2104.04670/) (2021)
- [Knowing What, How and Why: A Near Complete Solution for Aspect-based Sentiment Analysis](https://mlsanity.com/paper/1911.01616/) (2019)
- [A Unified Generative Framework for Aspect-Based Sentiment Analysis](https://mlsanity.com/paper/2106.04300/) (2021)
- [The Turking Test: Can Language Models Understand Instructions?](https://mlsanity.com/paper/2010.11982/) (2020)
- [Learning from Task Descriptions](https://mlsanity.com/paper/2011.08115/) (2020)
- [When Can Models Learn From Explanations? A Formal Framework for Understanding the Roles of Explanation Data](https://mlsanity.com/paper/2102.02201/) (2021)
- [Aspect Based Sentiment Analysis with Gated Convolutional Networks](https://mlsanity.com/paper/1805.07043/) (2018)
- [A Joint Training Dual-MRC Framework for Aspect Based Sentiment Analysis](https://mlsanity.com/paper/2101.00816/) (2021)
- [Hierarchical Task Learning from Language Instructions with Unified Transformers and Self-Monitoring](https://mlsanity.com/paper/2106.03427/) (2021)
- [Open-Domain Targeted Sentiment Analysis via Span-Based Extraction and Classification](https://mlsanity.com/paper/1906.03820/) (2019)
- [Code Completion with Neural Attention and Pointer Networks](https://mlsanity.com/paper/1711.09573/) (2017)
- [Bidirectional Machine Reading Comprehension for Aspect Sentiment Triplet Extraction](https://mlsanity.com/paper/2103.07665/) (2021)
- [Neural-Guided Deductive Search for Real-Time Program Synthesis from Examples](https://mlsanity.com/paper/1804.01186/) (2018)

## Built on by (most cited first, 23 of 23)

- [Affective Computing in the Era of Large Language Models: A Survey from the NLP Perspective](https://mlsanity.com/paper/2408.04638/) (2024)
- [TarGEN: Targeted Data Generation with Large Language Models](https://mlsanity.com/paper/2310.17876/) (2023)
- [MEMO-Bench: A Multiple Benchmark for Text-to-Image and Multimodal Large Language Models on Human Emotion Analysis](https://mlsanity.com/paper/2411.11235/) (2024)
- [OpinioRAG: Towards Generating User-Centric Opinion Highlights from Large-scale Online Reviews](https://mlsanity.com/paper/2509.00285/) (2025)
- [EDM3: Event Detection as Multi-task Text Generation](https://mlsanity.com/paper/2305.16357/) (2023)
- [A Novel Counterfactual Data Augmentation Method for Aspect-Based Sentiment Analysis](https://mlsanity.com/paper/2306.11260/) (2023)
- [Fine-tuning multilingual language models in Twitter/X sentiment analysis: a study on Eastern-European V4 languages](https://mlsanity.com/paper/2408.02044/) (2024)
- [Instruct-DeBERTa: A Hybrid Approach for Aspect-based Sentiment Analysis on Textual Reviews](https://mlsanity.com/paper/2408.13202/) (2024)
- [When LLMs Team Up: The Emergence of Collaborative Affective Computing](https://mlsanity.com/paper/2506.01698/) (2025)
- [From Annotation to Adaptation: Metrics, Synthetic Data, and Aspect Extraction for Aspect-Based Sentiment Analysis with Large Language Models](https://mlsanity.com/paper/2503.20715/) (2025)
- [Efficacy of Machine-Generated Instructions](https://mlsanity.com/paper/2312.14423/) (2023)
- [Towards a Unified Multimodal Reasoning Framework](https://mlsanity.com/paper/2312.15021/) (2023)
- [Extensible Multi-Granularity Fusion Network and Transferable Curriculum Learning for Aspect-based Sentiment Analysis](https://mlsanity.com/paper/2402.07787/) (2024)
- [SILC-EFSA: Self-aware In-context Learning Correction for Entity-level Financial Sentiment Analysis](https://mlsanity.com/paper/2412.19140/) (2024)
- [Leveraging Deep Neural Networks for Aspect-Based Sentiment Classification](https://mlsanity.com/paper/2503.12803/) (2025)
- [Multi-domain Multilingual Sentiment Analysis in Industry: Predicting Aspect-based Opinion Quadruples](https://mlsanity.com/paper/2505.10389/) (2025)
- [Large-Scale Aspect-Based Sentiment Analysis with Reasoning-Infused LLMs](https://mlsanity.com/paper/2601.03940/) (2026)
- [Label-Consistent Data Generation for Aspect-Based Sentiment Analysis Using LLM Agents](https://mlsanity.com/paper/2602.16379/) (2026)
- [From Prediction to Justification: Aligning Sentiment Reasoning with Human Rationale via Reinforcement Learning](https://mlsanity.com/paper/2604.13398/) (2026)
- [Task-Routed Mixture-of-Experts with Cognitive Appraisal for Implicit Sentiment Analysis](https://mlsanity.com/paper/2605.20916/) (2026)
- [Shifting from Discrete to Continuous Reference Data: QSM-Derived Horizontal Tree Biomass Distribution for Deep Learning Biomass Estimation](https://mlsanity.com/paper/2607.05260/) (2026)
- [STAR-OPD: Structured Aspect-Cascade-Aware On-Policy Reward Distillation for ABSA Quadruple Extraction](https://mlsanity.com/paper/2608.20831/) (2026)
- [Dependency Reasoning for Aspect Sentiment Quadruple Prediction](https://mlsanity.com/paper/2610.14873/) (2026)

## Abstract (arXiv)

We introduce InstructABSA, an instruction learning paradigm for Aspect-Based Sentiment Analysis (ABSA) subtasks. Our method introduces positive, negative, and neutral examples to each training sample, and instruction tune the model (Tk-Instruct) for ABSA subtasks, yielding significant performance improvements. Experimental results on the Sem Eval 2014, 15, and 16 datasets demonstrate that InstructABSA outperforms the previous state-of-the-art (SOTA) approaches on Term Extraction (ATE), Sentiment Classification(ATSC) and Sentiment Pair Extraction (ASPE) subtasks. In particular, InstructABSA outperforms the previous state-of-the-art (SOTA) on the Rest14 ATE subtask by 5.69% points, the Rest15 ATSC subtask by 9.59% points, and the Lapt14 AOPE subtask by 3.37% points, surpassing 7x larger models. We also get competitive results on AOOE, AOPE, and AOSTE subtasks indicating strong generalization ability to all subtasks. Exploring sample efficiency reveals that just 50% train data is required to get competitive results with other instruction tuning approaches. Lastly, we assess the quality of instructions and observe that InstructABSA's performance experiences a decline of ~10% when adding misleading examples.