Cross-lingual sentiment analysis is a challenging task in natural language processing due to the linguistic diversity across different languages. Existing approaches often struggle to accurately transfer sentiment knowledge between languages with distinct syntactic and semantic structures. In this research, we propose a novel approach called "KarakaCross" for cross-lingual sentiment analysis. Inspired by the Karaka theory, which models the semantic roles of words in sentences, our method leverages semantic role labeling and cross-lingual transfer learning techniques. The KarakaCross approach enables the alignment of sentiment-related semantic roles across languages, facilitating the transfer of sentiment knowledge. We conduct extensive experiments on multilingual datasets, demonstrating the effectiveness of KarakaCross in achieving superior cross-lingual sentiment analysis performance compared to state-of-the-art methods. Our research contributes to advancing the field of cross-lingual sentiment analysis and offers new insights into leveraging semantic role information for better sentiment transfer between languages.

Cross-lingual sentiment analysis, sentiment transfer, cross-lingual transfer learning

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