Abstract
Multilingual sentiment analysis refers to the process of sentiment scoring while gathering insights from data in different languages. Many research studies have been conducted to perform multilingual sentiment analysis. However, most of these studies focus on the short-distance semantics which consists in modeling local consecutive word sequences. In this work, we consider the global word co-occurrence in the whole corpus, which capture both short- and long-distance semantics, to convey more meaningful insights for the analysis. We propose an approach called MSA-GCN (Multilingual Sentiment Analysis based on Graph Convolutional Network) while supporting both short- and long-distance semantics. We build a single heterogeneous text graph for a multilingual corpus based on sequential, semantic, and statistical information. Then, a slightly deep graph convolutional network learns embeddings for all nodes in a semi-supervised manner. Extensive experiments are carried out on various datasets, and the results demonstrate the effectiveness of the proposed approach.
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Acknowledgements
The authors would like to thank Dr.Mohammed Amine Koulali for his fruitful discussions and comments on the earlier versions of this paper.
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Mercha, E.M., Benbrahim, H., Erradi, M. (2023). SlideGCN: Slightly Deep Graph Convolutional Network for Multilingual Sentiment Analysis. In: Rojas, I., Joya, G., Catala, A. (eds) Advances in Computational Intelligence. IWANN 2023. Lecture Notes in Computer Science, vol 14135. Springer, Cham. https://doi.org/10.1007/978-3-031-43078-7_8
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