Heterogeneous text graph for comprehensive multilingual sentiment analysis: capturing short- and long-distance semantics.

ABSTRACT: Multilingual sentiment analysis (MSA) involves the task of comprehending people's opinions, sentiments, and emotions in multilingual written texts. This task has garnered considerable attention due to its importance in extracting insights for decision-making across diverse fields such as marketing, finance, and politics. Several studies have explored MSA using deep learning methods. Nonetheless, a majority of these studies depend on sequential-based approaches, which focus on capturing short-distance semantics within adjacent word sequences, but they overlook long-distance semantics, which can provide more profound insights for analysis. In this work, we propose an approach for multilingual sentiment analysis, namely MSA-GCN, leveraging a graph convolutional network to effectively capture

SUBMITTER: Mercha EM 

PROVIDER: S-EPMC10909185 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature