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

Area of Science:

• Natural Language Processing
• Artificial Intelligence
• Computational Linguistics

Background:

• Multilingual sentiment analysis (MSA) is crucial for extracting opinions from diverse texts across various domains.
• Existing deep learning methods often rely on sequential approaches, overlooking long-distance semantic relationships vital for deeper insights.
• The need for advanced techniques to capture comprehensive semantic information in multilingual contexts is evident.

Purpose of the Study:

• To propose a novel approach, MSA-GCN, for multilingual sentiment analysis that effectively captures both short- and long-distance semantics.
• To leverage graph convolutional networks (GCNs) for a more profound understanding of opinions in multilingual text.
• To enhance the accuracy and robustness of sentiment analysis across different languages.

Main Methods:

• Developed MSA-GCN, a method utilizing a unified heterogeneous text graph to model multilingual sentiment analysis corpora.
• Employed a slightly deep graph convolutional network to learn predictive representations, facilitating cross-lingual transfer learning.
• Conducted extensive experiments on benchmark datasets including MARC, IMDB, Allociné, and Muchocine.

Main Results:

• MSA-GCN significantly outperformed baseline models across multiple language combinations and datasets (p-value < 0.05).
• The approach demonstrated robust performance against language variations, indicating its generalizability.
• Achieved superior results in capturing both short- and long-distance semantic dependencies.

Conclusions:

• MSA-GCN offers a superior approach to multilingual sentiment analysis by effectively integrating short- and long-distance semantic information.
• The graph convolutional network architecture proves effective in enhancing sentiment analysis performance and robustness across languages.
• This study highlights the potential of GCNs for advancing natural language understanding tasks in multilingual settings.