# Leveraging Deep Graph-Based Text Representation for Sentiment Polarity Applications

## TL;DR

This paper introduces a novel deep graph-based text representation method that includes stop words and leverages representation learning for sentiment analysis, outperforming existing approaches without relying on pre-trained embeddings.

## Contribution

The paper presents a new sentence-level graph-based text representation incorporating stop words and a deep learning framework for improved sentiment classification.

## Key findings

- Outperforms existing sentiment analysis methods on benchmark datasets
- Does not depend on pre-trained word embeddings
- Effective across different datasets

## Abstract

Over the last few years, machine learning over graph structures has manifested a significant enhancement in text mining applications such as event detection, opinion mining, and news recommendation. One of the primary challenges in this regard is structuring a graph that encodes and encompasses the features of textual data for the effective machine learning algorithm. Besides, exploration and exploiting of semantic relations is regarded as a principal step in text mining applications. However, most of the traditional text mining methods perform somewhat poor in terms of employing such relations. In this paper, we propose a sentence-level graph-based text representation which includes stop words to consider semantic and term relations. Then, we employ a representation learning approach on the combined graphs of sentences to extract the latent and continuous features of the documents. Eventually, the learned features of the documents are fed into a deep neural network for the sentiment classification task. The experimental results demonstrate that the proposed method substantially outperforms the related sentiment analysis approaches based on several benchmark datasets. Furthermore, our method can be generalized on different datasets without any dependency on pre-trained word embeddings.