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

Hiroshi Kanayama, Yang Zhao, Ran Iwamoto, Takuya Ohko

Abstract: This paper exploits a sentiment extractor supported by syntactic and lexical resources to enhance multilingual sentiment classification solved through the generative approach, without retraining LLMs. By adding external information of words and phrases that have positive/negative polarities, the multilingual sentiment classification error was reduced by up to 33 points, and the combination of two approaches performed best especially in high-performing pairs of LLMs and languages.