Sentiment analysis of Chinese ancient poetry based on multidimensional knowledge attention

Abstract Poetry was a unique literary genre in ancient China as an important way to express sentiments. Chinese ancient poetry not only has simple words, strict meters, and rich semantic relationships, but also widely use rhetorical techniques such as simile and personification, as well as metaphorical means such as allusion and imagery, which makes it difficult to understand their implicit sentiments quickly and accurately. Therefore, this article attempts to make full use of the semantic features of Chinese ancient poetry text and the knowledge feature of related domains under the guidance of the research paradigm of digital intelligence integration and, based on which, proposed Sentiment Analysis Model of Chinese Ancient Poetry based on Multidimensional Knowledge Attention. This model extracts semantic features from Chinese ancient poetry text, meanwhile designs a multidimensional knowledge attention to extract knowledge features from the knowledge base of Chinese ancient poetry. The recognition of Chinese ancient poetry sentiment is performed by integrating the textual feature and knowledge feature. Comparison experiments on the open ancient poetry corpus verified the effectiveness of the proposed model, and the ablation experiment explored the importance of different knowledge to the sentiment analysis result of Chinese ancient poetry.