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.
Anthology ID:
2024.findings-acl.286
Volume:
Findings of the Association for Computational Linguistics ACL 2024
Month:
August
Year:
2024
Address:
Bangkok, Thailand and virtual meeting
Editors:
Lun-Wei Ku, Andre Martins, Vivek Srikumar
Venue:
Findings
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
4810–4817
Language:
URL:
https://aclanthology.org/2024.findings-acl.286
DOI:
10.18653/v1/2024.findings-acl.286
Bibkey:
Cite (ACL):
Hiroshi Kanayama, Yang Zhao, Ran Iwamoto, and Takuya Ohko. 2024. Incorporating Syntax and Lexical Knowledge to Multilingual Sentiment Classification on Large Language Models. In Findings of the Association for Computational Linguistics ACL 2024, pages 4810–4817, Bangkok, Thailand and virtual meeting. Association for Computational Linguistics.
Cite (Informal):
Incorporating Syntax and Lexical Knowledge to Multilingual Sentiment Classification on Large Language Models (Kanayama et al., Findings 2024)
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PDF:
https://aclanthology.org/2024.findings-acl.286.pdf