# Text classification optimization algorithm based on graph neural network

arXiv: https://arxiv.org/abs/2408.15257 · HTML: https://mlsanity.com/paper/2408.15257/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 25 references that are in the map.
- First posted on arXiv 9 Aug 2024.

## Builds on (most cited first, 13 of 13)

- [Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling](https://mlsanity.com/paper/1412.3555/) (2014)
- [Survival Prediction Across Diverse Cancer Types Using Neural Networks](https://mlsanity.com/paper/2404.08713/) (2024)
- [Efficiency optimization of large-scale language models based on deep learning in natural language processing tasks](https://mlsanity.com/paper/2405.11704/) (2024)
- [Learning Longer Memory in Recurrent Neural Networks](https://mlsanity.com/paper/1412.7753/) (2014)
- [Application of Multimodal Fusion Deep Learning Model in Disease Recognition](https://mlsanity.com/paper/2406.18546/) (2024)
- [Enhancing Medical Imaging with GANs Synthesizing Realistic Images from Limited Data](https://mlsanity.com/paper/2406.18547/) (2024)
- [Advanced Financial Fraud Detection Using GNN-CL Model](https://mlsanity.com/paper/2407.06529/) (2024)
- [An Enhanced Encoder-Decoder Network Architecture for Reducing Information Loss in Image Semantic Segmentation](https://mlsanity.com/paper/2406.01605/) (2024)
- [Exploration of Multi-Scale Image Fusion Systems in Intelligent Medical Image Analysis](https://mlsanity.com/paper/2406.18548/) (2024)
- [Application of Computer Deep Learning Model in Diagnosis of Pulmonary Nodules](https://mlsanity.com/paper/2406.13205/) (2024)
- [Research on Early Warning Model of Cardiovascular Disease Based on Computer Deep Learning](https://mlsanity.com/paper/2406.08864/) (2024)
- [Research on Disease Prediction Model Construction Based on Computer AI deep Learning Technology](https://mlsanity.com/paper/2406.16982/) (2024)
- [Research on Feature Extraction Data Processing System For MRI of Brain Diseases Based on Computer Deep Learning](https://mlsanity.com/paper/2406.16981/) (2024)

## Built on by (most cited first, 8 of 8)

- [Contrastive Learning for Knowledge-Based Question Generation in Large Language Models](https://mlsanity.com/paper/2409.13994/) (2024)
- [Applying Hybrid Graph Neural Networks to Strengthen Credit Risk Analysis](https://mlsanity.com/paper/2410.04283/) (2024)
- [Balancing Innovation and Privacy: Data Security Strategies in Natural Language Processing Applications](https://mlsanity.com/paper/2410.08553/) (2024)
- [Optimizing News Text Classification with Bi-LSTM and Attention Mechanism for Efficient Data Processing](https://mlsanity.com/paper/2409.15576/) (2024)
- [Deep Learning-Based Channel Squeeze U-Structure for Lung Nodule Detection and Segmentation](https://mlsanity.com/paper/2409.13868/) (2024)
- [Graph Neural Network Framework for Sentiment Analysis Using Syntactic Feature](https://mlsanity.com/paper/2409.14000/) (2024)
- [Axial Attention Transformer Networks: A New Frontier in Breast Cancer Detection](https://mlsanity.com/paper/2409.12347/) (2024)
- [Few-Shot Learning with Adaptive Weight Masking in Conditional GANs](https://mlsanity.com/paper/2412.03105/) (2024)

## Abstract (arXiv)

In the field of natural language processing, text classification, as a basic task, has important research value and application prospects. Traditional text classification methods usually rely on feature representations such as the bag of words model or TF-IDF, which overlook the semantic connections between words and make it challenging to grasp the deep structural details of the text. Recently, GNNs have proven to be a valuable asset for text classification tasks, thanks to their capability to handle non-Euclidean data efficiently. However, the existing text classification methods based on GNN still face challenges such as complex graph structure construction and high cost of model training. This paper introduces a text classification optimization algorithm utilizing graph neural networks. By introducing adaptive graph construction strategy and efficient graph convolution operation, the accuracy and efficiency of text classification are effectively improved. The experimental results demonstrate that the proposed method surpasses traditional approaches and existing GNN models across multiple public datasets, highlighting its superior performance and feasibility for text classification tasks.