利用大型语言模型进行立陶宛在线评论的情感分析

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

情感分析是自然语言处理（NLP）领域中一个广泛研究的领域，由于自动化解决方案的出现，它吸引了广泛的兴趣。尽管如此，由于语言的固有复杂性和情感的主观性，这项任务仍然具有挑战性。对于立陶宛语等较少研究和资源较少的语言来说，情况更加具有挑战性。我们对现有的立陶宛语NLP研究进行的审查表明，传统的机器学习方法和分类算法对这一任务的效果有限。在这项工作中，我们致力于处理来自多个领域的基于五星评级的立陶宛在线评论的情感分析，我们对其进行了收集和清洗。我们首次将变压器模型应用于这一任务，探索了预训练的多语言大型语言模型（LLMs）的能力，特别是着重于对BERT和T5模型进行微调。鉴于任务的固有困难性，经过微调的模型表现相当不错，特别是当情感本身不太模糊时：对于最受欢迎的一星和五星评论，测试识别准确率分别为80.74%和89.61%。它们明显优于当前商业最先进的通用LLM GPT-4。我们将我们的经过微调的LLMs公开分享在网上。

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.