Abstract
Sentiment Analysis, known also as opinion mining, discover and extract subjective information from source data allowing businesses to better understand the social sentiment around their products or services. The multilingual characteristics of these data require efficient multilingual sentiment analysis tools. In this work, we propose a graph-based approach for multilingual sentiment analysis. We construct a single heterogeneous text graph based on semantic, sequential, and statistical information to represent the entire multilingual corpus. Then, a graph convolution network learns predictive representation for nodes in a semi-supervised manner. Extensive experiments in real-world multilingual sentiment analysis dataset, demonstrate the effectiveness of the proposed approach. Also, it significantly outperforms the baseline models.
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The authors would like to thank HENCEFORTHÂ for its financial support for this research project.
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Mercha, E.M., Benbrahim, H., Erradi, M. (2023). Graph Convolutional Network for Multilingual Sentiment Analysis. In: Idrissi, A. (eds) Modern Artificial Intelligence and Data Science. Studies in Computational Intelligence, vol 1102. Springer, Cham. https://doi.org/10.1007/978-3-031-33309-5_9
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