# My Words Imply Your Opinion: Reader Agent-based Propagation Enhancement for Personalized Implicit Emotion Analysis

arXiv: https://arxiv.org/abs/2412.07367 · HTML: https://mlsanity.com/paper/2412.07367/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 38 references that are in the map.
- First posted on arXiv 10 Dec 2024.

## Builds on (most cited first, 14 of 14)

- [LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation](https://mlsanity.com/paper/2002.02126/) (2020)
- [ChatGLM: A Family of Large Language Models from GLM-130B to GLM-4 All Tools](https://mlsanity.com/paper/2406.12793/) (2024)
- [GLM: General Language Model Pretraining with Autoregressive Blank Infilling](https://mlsanity.com/paper/2103.10360/) (2021)
- [Using Large Language Models to Simulate Multiple Humans and Replicate Human Subject Studies](https://mlsanity.com/paper/2208.10264/) (2022)
- [Reasoning Implicit Sentiment with Chain-of-Thought Prompting](https://mlsanity.com/paper/2305.11255/) (2023)
- [Can Large Language Model Agents Simulate Human Trust Behavior?](https://mlsanity.com/paper/2402.04559/) (2024)
- [Learning Implicit Sentiment in Aspect-based Sentiment Analysis with Supervised Contrastive Pre-Training](https://mlsanity.com/paper/2111.02194/) (2021)
- [UserIdentifier: Implicit User Representations for Simple and Effective Personalized Sentiment Analysis](https://mlsanity.com/paper/2110.00135/) (2021)
- [Multimodal Multi-loss Fusion Network for Sentiment Analysis](https://mlsanity.com/paper/2308.00264/) (2023)
- [Exploiting Simulated User Feedback for Conversational Search: Ranking, Rewriting, and Beyond](https://mlsanity.com/paper/2304.13874/) (2023)
- [Multi-view Graph Learning by Joint Modeling of Consistency and Inconsistency](https://mlsanity.com/paper/2008.10208/) (2020)
- [Causal Intervention Improves Implicit Sentiment Analysis](https://mlsanity.com/paper/2208.09329/) (2022)
- [DEEM: Dynamic Experienced Expert Modeling for Stance Detection](https://mlsanity.com/paper/2402.15264/) (2024)
- [Your Large Language Model is Secretly a Fairness Proponent and You Should Prompt it Like One](https://mlsanity.com/paper/2402.12150/) (2024)

## Built on by (most cited first, 4 of 4)

- [How to Interpret Agent Behavior](https://mlsanity.com/paper/2605.13625/) (2026)
- [PERM: Psychology-grounded Empathetic Reward Modeling for Large Language Models](https://mlsanity.com/paper/2601.10532/) (2026)
- [Are Emotion and Rhetoric Neurons in LLM? Neuron Recognition and Adaptive Masking for Emotion-Rhetoric Prediction Steering](https://mlsanity.com/paper/2604.17255/) (2026)
- [PERCEIVE: A Benchmark for Personalized Emotion and Communication Behavior Understanding on Social Media](https://mlsanity.com/paper/2605.12525/) (2026)

## Abstract (arXiv)

The subtlety of emotional expressions makes implicit emotion analysis (IEA) particularly sensitive to user-specific characteristics. Current studies personalize emotion analysis by focusing on the author but neglect the impact of the intended reader on implicit emotional feedback. In this paper, we introduce Personalized IEA (PIEA) and present the RAPPIE model, which addresses subjective variability by incorporating reader feedback. In particular, (1) we create reader agents based on large language models to simulate reader feedback, overcoming the issue of ``spiral of silence effect'' and data incompleteness of real reader reaction. (2) We develop a role-aware multi-view graph learning to model the emotion interactive propagation process in scenarios with sparse reader information. (3) We construct two new PIEA datasets covering English and Chinese social media with detailed user metadata, addressing the text-centric limitation of existing datasets. Extensive experiments show that RAPPIE significantly outperforms state-of-the-art baselines, demonstrating the value of incorporating reader feedback in PIEA.