Sunny, J.; Padraig, T.; Terry, R.; Ali, W. Enhanced Sentiment Analysis with Syntactic Dependency and Advanced Graph Convolution Model. Preprints2023, 2023111877. https://doi.org/10.20944/preprints202311.1877.v1
APA Style
Sunny, J., Padraig, T., Terry, R., & Ali, W. (2023). Enhanced Sentiment Analysis with Syntactic Dependency and Advanced Graph Convolution Model. Preprints. https://doi.org/10.20944/preprints202311.1877.v1
Chicago/Turabian Style
Sunny, J., Roggie Terry and Woods Ali. 2023 "Enhanced Sentiment Analysis with Syntactic Dependency and Advanced Graph Convolution Model" Preprints. https://doi.org/10.20944/preprints202311.1877.v1
Abstract
This paper presents the Advanced Syntactic-Graph Convolutional Model (ASGCM), a pioneering approach in Aspect-Based Sentiment Analysis (ABSA) that integrates syntactic dependency features within a graph convolution framework. ASGCM stands out for its novel use of dependency edge encoding and tag-based graph convolutions, providing a fine-grained analysis of sentiments associated with specific aspects in text. This model meticulously captures the intricacies of syntactic structures, thereby offering enhanced precision in sentiment analysis. Notably, ASGCM incorporates a dual-layer graph convolution system: one layer processes syntactic dependencies (edges), while the other interprets semantic roles (tags), ensuring a comprehensive understanding of both structural and contextual elements in text. We rigorously tested ASGCM on multiple datasets, including both English and Chinese languages, and our findings reveal a significant improvement in sentiment classification accuracy compared to existing models. The versatility of ASGCM makes it a robust tool for diverse linguistic environments, setting a new standard for ABSA methodologies.
Keywords
Sentiment Analysis; Dependency Syntax; Graph Convolutional Model
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
