# SentiPrompt: Sentiment Knowledge Enhanced Prompt-Tuning for Aspect-Based Sentiment Analysis

arXiv: https://arxiv.org/abs/2109.08306 · HTML: https://mlsanity.com/paper/2109.08306/

## Key facts

- Cited by 23 papers in the map (data through 14 Oct 2026).
- Builds on 28 of its 44 references that are in the map.
- First posted on arXiv 17 Sep 2021.

## Builds on (most cited first, 28 of 28)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://mlsanity.com/paper/1910.13461/) (2019)
- [Prefix-Tuning: Optimizing Continuous Prompts for Generation](https://mlsanity.com/paper/2101.00190/) (2021)
- [Language Models as Knowledge Bases?](https://mlsanity.com/paper/1909.01066/) (2019)
- [Making Pre-trained Language Models Better Few-shot Learners](https://mlsanity.com/paper/2012.15723/) (2020)
- [How Can We Know What Language Models Know?](https://mlsanity.com/paper/1911.12543/) (2019)
- [Exploiting Cloze Questions for Few Shot Text Classification and Natural Language Inference](https://mlsanity.com/paper/2001.07676/) (2020)
- [Pointer Networks](https://mlsanity.com/paper/1506.03134/) (2015)
- [PTR: Prompt Tuning with Rules for Text Classification](https://mlsanity.com/paper/2105.11259/) (2021)
- [Commonsense Knowledge Mining from Pretrained Models](https://mlsanity.com/paper/1909.00505/) (2019)
- [Knowledgeable Prompt-tuning: Incorporating Knowledge into Prompt Verbalizer for Text Classification](https://mlsanity.com/paper/2108.02035/) (2021)
- [oLMpics -- On what Language Model Pre-training Captures](https://mlsanity.com/paper/1912.13283/) (2019)
- [Knowing What, How and Why: A Near Complete Solution for Aspect-based Sentiment Analysis](https://mlsanity.com/paper/1911.01616/) (2019)
- [Differentiable Prompt Makes Pre-trained Language Models Better Few-shot Learners](https://mlsanity.com/paper/2108.13161/) (2021)
- [Relational Graph Attention Network for Aspect-based Sentiment Analysis](https://mlsanity.com/paper/2004.12362/) (2020)
- [A Unified Generative Framework for Aspect-Based Sentiment Analysis](https://mlsanity.com/paper/2106.04300/) (2021)
- [Position-Aware Tagging for Aspect Sentiment Triplet Extraction](https://mlsanity.com/paper/2010.02609/) (2020)
- [Effective LSTMs for Target-Dependent Sentiment Classification](https://mlsanity.com/paper/1512.01100/) (2015)
- [A Joint Training Dual-MRC Framework for Aspect Based Sentiment Analysis](https://mlsanity.com/paper/2101.00816/) (2021)
- [Prompt-Learning for Fine-Grained Entity Typing](https://mlsanity.com/paper/2108.10604/) (2021)
- [Learning Span-Level Interactions for Aspect Sentiment Triplet Extraction](https://mlsanity.com/paper/2107.12214/) (2021)
- [An Interactive Multi-Task Learning Network for End-to-End Aspect-Based Sentiment Analysis](https://mlsanity.com/paper/1906.06906/) (2019)
- [Aspect Term Extraction with History Attention and Selective Transformation](https://mlsanity.com/paper/1805.00760/) (2018)
- [AliCG: Fine-grained and Evolvable Conceptual Graph Construction for Semantic Search at Alibaba](https://mlsanity.com/paper/2106.01686/) (2021)
- [Conditional Augmentation for Aspect Term Extraction via Masked Sequence-to-Sequence Generation](https://mlsanity.com/paper/2004.14769/) (2020)
- [Latent Opinions Transfer Network for Target-Oriented Opinion Words Extraction](https://mlsanity.com/paper/2001.01989/) (2020)
- [Introducing Syntactic Structures into Target Opinion Word Extraction with Deep Learning](https://mlsanity.com/paper/2010.13378/) (2020)
- [Interventional Aspect-Based Sentiment Analysis](https://mlsanity.com/paper/2104.11681/) (2021)

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

- [P-Tuning v2: Prompt Tuning Can Be Comparable to Fine-tuning Universally Across Scales and Tasks](https://mlsanity.com/paper/2110.07602/) (2021)
- [A Survey of Large Language Models for Healthcare: from Data, Technology, and Applications to Accountability and Ethics](https://mlsanity.com/paper/2310.05694/) (2023)
- [OntoProtein: Protein Pretraining With Gene Ontology Embedding](https://mlsanity.com/paper/2201.11147/) (2022)
- [Paradigm Shift in Natural Language Processing](https://mlsanity.com/paper/2109.12575/) (2021)
- [Towards Unified Prompt Tuning for Few-shot Text Classification](https://mlsanity.com/paper/2205.05313/) (2022)
- [DeepKE: A Deep Learning Based Knowledge Extraction Toolkit for Knowledge Base Population](https://mlsanity.com/paper/2201.03335/) (2022)
- [A Contrastive Cross-Channel Data Augmentation Framework for Aspect-based Sentiment Analysis](https://mlsanity.com/paper/2204.07832/) (2022)
- [UniMEEC: Towards Unified Multimodal Emotion Recognition and Emotion Cause](https://mlsanity.com/paper/2404.00403/) (2024)
- [Contrastive Demonstration Tuning for Pre-trained Language Models](https://mlsanity.com/paper/2204.04392/) (2022)
- [LOGEN: Few-shot Logical Knowledge-Conditioned Text Generation with Self-training](https://mlsanity.com/paper/2112.01404/) (2021)
- [Contextual Information and Commonsense Based Prompt for Emotion Recognition in Conversation](https://mlsanity.com/paper/2207.13254/) (2022)
- [SynGen: A Syntactic Plug-and-play Module for Generative Aspect-based Sentiment Analysis](https://mlsanity.com/paper/2302.13032/) (2023)
- [Prompt Based Tri-Channel Graph Convolution Neural Network for Aspect Sentiment Triplet Extraction](https://mlsanity.com/paper/2312.11152/) (2023)
- [CONTRASTE: Supervised Contrastive Pre-training With Aspect-based Prompts For Aspect Sentiment Triplet Extraction](https://mlsanity.com/paper/2310.15577/) (2023)
- [AMPLE: Emotion-Aware Multimodal Fusion Prompt Learning for Fake News Detection](https://mlsanity.com/paper/2410.15591/) (2024)
- [Input-length-shortening and text generation via attention values](https://mlsanity.com/paper/2303.07585/) (2023)
- [KESA: A Knowledge Enhanced Approach For Sentiment Analysis](https://mlsanity.com/paper/2202.12093/) (2022)
- [Soft Prompt Guided Joint Learning for Cross-Domain Sentiment Analysis](https://mlsanity.com/paper/2303.00815/) (2023)
- [TransPrompt v2: A Transferable Prompting Framework for Cross-task Text Classification](https://mlsanity.com/paper/2308.15010/) (2023)
- [Entity-Aspect-Opinion-Sentiment Quadruple Extraction for Fine-grained Sentiment Analysis](https://mlsanity.com/paper/2311.16678/) (2023)
- [Embarrassingly Simple Unsupervised Aspect Based Sentiment Tuple Extraction](https://mlsanity.com/paper/2404.13751/) (2024)
- [Detecting Statements in Text: A Domain-Agnostic Few-Shot Solution](https://mlsanity.com/paper/2405.05705/) (2024)
- [Unravelling Technical debt topics through Time, Programming Languages and Repository](https://mlsanity.com/paper/2504.11714/) (2025)

## Abstract (arXiv)

Aspect-based sentiment analysis (ABSA) is an emerging fine-grained sentiment analysis task that aims to extract aspects, classify corresponding sentiment polarities and find opinions as the causes of sentiment. The latest research tends to solve the ABSA task in a unified way with end-to-end frameworks. Yet, these frameworks get fine-tuned from downstream tasks without any task-adaptive modification. Specifically, they do not use task-related knowledge well or explicitly model relations between aspect and opinion terms, hindering them from better performance. In this paper, we propose SentiPrompt to use sentiment knowledge enhanced prompts to tune the language model in the unified framework. We inject sentiment knowledge regarding aspects, opinions, and polarities into prompt and explicitly model term relations via constructing consistency and polarity judgment templates from the ground truth triplets. Experimental results demonstrate that our approach can outperform strong baselines on Triplet Extraction, Pair Extraction, and Aspect Term Extraction with Sentiment Classification by a notable margin.