Incorporating Syntax and Lexical Knowledge to Multilingual Sentiment Classification on Large Language Models

This paper exploits a sentiment extractor supported by syntactic and lexical resources to enhance multilingual sentiment classification solved through the generative approach. By adding external information of words and phrases that have positive/negative polarities, the sentiment classification error was reduced by 5 to 23 points, and it was especially effective in poorly performing combinations of languages and models.