Deep Emotions Across Languages: A Novel Approach for Sentiment
  Propagation in Multilingual WordNets
Jan Kocoń
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.