利用大型語言模型進行立陶宛線上評論的情感分析

Sentiment Analysis of Lithuanian Online Reviews Using Large Language Models

July 29, 2024

作者: Brigita Vileikytė, Mantas Lukoševičius, Lukas Stankevičius

cs.AI

摘要

情感分析是自然語言處理（NLP）領域中被廣泛研究的一個範疇，由於自動化解決方案的出現，它引起了相當大的興趣。儘管如此，由於語言的固有複雜性和情感的主觀性，這項任務仍然具有挑戰性。對於立陶宛語等少有研究和資源的語言來說，情況更加困難。我們對現有的立陶宛語NLP研究進行了回顧，發現傳統機器學習方法和分類算法對於這一任務的效果有限。在這項工作中，我們處理了從多個領域收集並清理的立陶宛語五星級在線評論的情感分析。我們首次將Transformer模型應用於這一任務，探索了預訓練的多語言大型語言模型（LLMs）的能力，特別是專注於BERT和T5模型的微調。鑒於任務的困難性，經過微調的模型表現相當出色，特別是當情感本身不太模糊時：最受歡迎的一星和五星評論的測試識別準確率分別為80.74%和89.61%。它們明顯優於當前商業最先進的通用LLM GPT-4。我們將我們微調的LLMs公開分享在線。

English

Sentiment analysis is a widely researched area within Natural Language Processing (NLP), attracting significant interest due to the advent of automated solutions. Despite this, the task remains challenging because of the inherent complexity of languages and the subjective nature of sentiments. It is even more challenging for less-studied and less-resourced languages such as Lithuanian. Our review of existing Lithuanian NLP research reveals that traditional machine learning methods and classification algorithms have limited effectiveness for the task. In this work, we address sentiment analysis of Lithuanian five-star-based online reviews from multiple domains that we collect and clean. We apply transformer models to this task for the first time, exploring the capabilities of pre-trained multilingual Large Language Models (LLMs), specifically focusing on fine-tuning BERT and T5 models. Given the inherent difficulty of the task, the fine-tuned models perform quite well, especially when the sentiments themselves are less ambiguous: 80.74% and 89.61% testing recognition accuracy of the most popular one- and five-star reviews respectively. They significantly outperform current commercial state-of-the-art general-purpose LLM GPT-4. We openly share our fine-tuned LLMs online.