Sentiment analysis involves using WordNets enriched with emotional metadata, which are valuable resources. However, manual annotation is time-consuming and expensive, resulting in only a few WordNet Lexical Units being annotated. This paper introduces two new techniques for automatically propagating sentiment annotations from a partially annotated WordNet to its entirety and to a WordNet in a different language: Multilingual Structured Synset Embeddings (MSSE) and Cross-Lingual Deep Neural Sentiment Propagation (CLDNS). We evaluated the proposed MSSE+CLDNS method extensively using Princeton WordNet and Polish WordNet, which have many inter-lingual relations. Our results show that the MSSE+CLDNS method outperforms existing propagation methods, indicating its effectiveness in enriching WordNets with emotional metadata across multiple languages. This work provides a solid foundation for large-scale, multilingual sentiment analysis and is valuable for academic research and practical applications.

翻译：情感分析依赖于带有情感元数据的WordNet资源。然而，人工标注耗时且成本高昂，导致仅有少数WordNet词条被标注。本文提出了两种新技术，用于将情感标注从部分标注的WordNet自动传播到完整WordNet以及其他语言的WordNet：多语言结构化同义词集嵌入（MSSE）与跨语言深度神经情感传播（CLDNS）。我们利用普林斯顿WordNet和波兰语WordNet（两者存在大量跨语言关联）对MSSE+CLDNS方法进行了全面评估。结果表明，该方法优于现有的传播技术，能够有效丰富多语种WordNet的情感元数据。本研究为大规模多语种情感分析奠定了坚实基础，对学术研究和实际应用均有重要价值。