# Super Characters: A Conversion from Sentiment Classification to Image Classification

arXiv: https://arxiv.org/abs/1810.07653 · HTML: https://mlsanity.com/paper/1810.07653/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Oct 2018.

## Built on by (most cited first, 9 of 9)

- [Between words and characters: A Brief History of Open-Vocabulary Modeling and Tokenization in NLP](https://mlsanity.com/paper/2112.10508/) (2021)
- [Robust Open-Vocabulary Translation from Visual Text Representations](https://mlsanity.com/paper/2104.08211/) (2021)
- [PIXAR: Auto-Regressive Language Modeling in Pixel Space](https://mlsanity.com/paper/2401.03321/) (2024)
- [GnetSeg: Semantic Segmentation Model Optimized on a 224mW CNN Accelerator Chip at the Speed of 318FPS](https://mlsanity.com/paper/2101.10444/) (2021)
- [GnetDet: Object Detection Optimized on a 224mW CNN Accelerator Chip at the Speed of 106FPS](https://mlsanity.com/paper/2103.15756/) (2021)
- [A Dynamic Weighted Tabular Method for Convolutional Neural Networks](https://mlsanity.com/paper/2205.10386/) (2022)
- [MIXAR: Scaling Autoregressive Pixel-based Language Models to Multiple Languages and Scripts](https://mlsanity.com/paper/2604.11575/) (2026)
- [Fuzzy Convolution Neural Networks for Tabular Data Classification](https://mlsanity.com/paper/2406.03506/) (2024)
- [Uncertainty in Semantic Language Modeling with PIXELS](https://mlsanity.com/paper/2509.19563/) (2025)

## Abstract (arXiv)

We propose a method named Super Characters for sentiment classification. This method converts the sentiment classification problem into image classification problem by projecting texts into images and then applying CNN models for classification. Text features are extracted automatically from the generated Super Characters images, hence there is no need of any explicit step of embedding the words or characters into numerical vector representations. Experimental results on large social media corpus show that the Super Characters method consistently outperforms other methods for sentiment classification and topic classification tasks on ten large social media datasets of millions of contents in four different languages, including Chinese, Japanese, Korean and English.