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.

本研究介绍了两种新技术（多语言结构化同义词嵌入和跨语言深度神经情感传播），用于自动将情感注释从部分注释的 WordNet 传播到整个 WordNet 以及不同语言的 WordNet。通过对普林斯顿 WordNet 和波兰 WordNet 进行广泛评估，研究结果表明该方法在跨多种语言丰富 WordNet 的情感元数据方面优于现有传播方法，为大规模、多语言情感分析提供了坚实的基础，对学术研究和实际应用具有重要价值。