# Phonetic-enriched Text Representation for Chinese Sentiment Analysis with Reinforcement Learning

arXiv: https://arxiv.org/abs/1901.07880 · HTML: https://mlsanity.com/paper/1901.07880/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Jan 2019.

## Built on by (most cited first, 5 of 5)

- [Deep-Attack over the Deep Reinforcement Learning](https://mlsanity.com/paper/2205.00807/) (2022)
- [SWE2: SubWord Enriched and Significant Word Emphasized Framework for Hate Speech Detection](https://mlsanity.com/paper/2409.16673/) (2024)
- [Video Sentiment Analysis with Bimodal Information-augmented Multi-Head Attention](https://mlsanity.com/paper/2103.02362/) (2021)
- [PGN: A perturbation generation network against deep reinforcement learning](https://mlsanity.com/paper/2312.12904/) (2023)
- [Picturized and Recited with Dialects: A Multimodal Chinese Representation Framework for Sentiment Analysis of Classical Chinese Poetry](https://mlsanity.com/paper/2505.13210/) (2025)

## Abstract (arXiv)

The Chinese pronunciation system offers two characteristics that distinguish it from other languages: deep phonemic orthography and intonation variations. We are the first to argue that these two important properties can play a major role in Chinese sentiment analysis. Particularly, we propose two effective features to encode phonetic information. Next, we develop a Disambiguate Intonation for Sentiment Analysis (DISA) network using a reinforcement network. It functions as disambiguating intonations for each Chinese character (pinyin). Thus, a precise phonetic representation of Chinese is learned. Furthermore, we also fuse phonetic features with textual and visual features in order to mimic the way humans read and understand Chinese text. Experimental results on five different Chinese sentiment analysis datasets show that the inclusion of phonetic features significantly and consistently improves the performance of textual and visual representations and outshines the state-of-the-art Chinese character level representations.