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<p>This paper presents LISA as a Language Independent Sentiment Analysis tool that exploits Graph Neural Networks, GNN, for sentiment analysis, SA, applications. To build up that analyzer two types of GNN are examined. These types are: (1) Graph convolutional Networks, GCN, and (2) Graph Attention Networks, GAT. The input text is transformed to a corresponding graph to bypass the multilingual differential representations between Natural Languages, NL. Accordingly, LISA includes input steam of text (e.g. tweets, reviews…), followed by a text to graph module which is connected to a tokenizer to determine the correct tokens and pass them to Word2vec embedding. The corresponding graph of the underlying text enters the GNN which has been implemented by either GCN or GAT to generate the output sentiment polarity (+ ve/−ve). The two GNN models are compared to explore the effect of attention on a SA. In addition, LISA performance is evaluated (using two multilingual datasets: amazon reviews, tweets). It has been found that its accuracy, F1 score and confusion matrices are in the same range of the state of the art (SOTA) framework.</p>

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LISA: language independent sentiment analysis using graph neural networks

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年12月29日

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  • Mohamed Zaki,
  • Basheer Youssef,
  • Salwa El-gamal,
  • Mohamed Abd-Elrahem

摘要

This paper presents LISA as a Language Independent Sentiment Analysis tool that exploits Graph Neural Networks, GNN, for sentiment analysis, SA, applications. To build up that analyzer two types of GNN are examined. These types are: (1) Graph convolutional Networks, GCN, and (2) Graph Attention Networks, GAT. The input text is transformed to a corresponding graph to bypass the multilingual differential representations between Natural Languages, NL. Accordingly, LISA includes input steam of text (e.g. tweets, reviews…), followed by a text to graph module which is connected to a tokenizer to determine the correct tokens and pass them to Word2vec embedding. The corresponding graph of the underlying text enters the GNN which has been implemented by either GCN or GAT to generate the output sentiment polarity (+ ve/−ve). The two GNN models are compared to explore the effect of attention on a SA. In addition, LISA performance is evaluated (using two multilingual datasets: amazon reviews, tweets). It has been found that its accuracy, F1 score and confusion matrices are in the same range of the state of the art (SOTA) framework.