# Human-Like Decision Making: Document-level Aspect Sentiment Classification via Hierarchical Reinforcement Learning

arXiv: https://arxiv.org/abs/1910.09260 · HTML: https://mlsanity.com/paper/1910.09260/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Oct 2019.

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

- [Challenges for Open-domain Targeted Sentiment Analysis](https://mlsanity.com/paper/2204.06893/) (2022)
- [Choice-75: A Dataset on Decision Branching in Script Learning](https://mlsanity.com/paper/2309.11737/) (2023)
- [Potential Impacts of Smart Homes on Human Behavior: A Reinforcement Learning Approach](https://mlsanity.com/paper/2102.13307/) (2021)
- [ChatASU: Evoking LLM's Reflexion to Truly Understand Aspect Sentiment in Dialogues](https://mlsanity.com/paper/2403.05326/) (2024)

## Abstract (arXiv)

Recently, neural networks have shown promising results on Document-level Aspect Sentiment Classification (DASC). However, these approaches often offer little transparency w.r.t. their inner working mechanisms and lack interpretability. In this paper, to simulating the steps of analyzing aspect sentiment in a document by human beings, we propose a new Hierarchical Reinforcement Learning (HRL) approach to DASC. This approach incorporates clause selection and word selection strategies to tackle the data noise problem in the task of DASC. First, a high-level policy is proposed to select aspect-relevant clauses and discard noisy clauses. Then, a low-level policy is proposed to select sentiment-relevant words and discard noisy words inside the selected clauses. Finally, a sentiment rating predictor is designed to provide reward signals to guide both clause and word selection. Experimental results demonstrate the impressive effectiveness of the proposed approach to DASC over the state-of-the-art baselines.