title: Sentiment Analysis of Lithuanian Online Reviews Using Large Language
    Models
summary: 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.
paper_authors._id: 66a866bacd3dd5ec13aad34c
paper_authors.name: Brigita Vileikytė
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-07-30T09:07:25.043Z
paper_authors._id: 65587d2323e43ac218a79ffc
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/noauth/ez80XQ_DER34OSgGtv_wv.jpeg
paper_authors.fullname: Brigita Vileikytė
paper_authors.type: user
paper_authors.user: brivil1
paper_authors._id: 66a866bacd3dd5ec13aad34d
paper_authors.name: Mantas Lukoševičius
paper_authors._id: 66a866bacd3dd5ec13aad34e
paper_authors.name: Lukas Stankevičius
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-07-30T09:07:40.972Z
paper_authors._id: 60801904237f7a0189f2a87a
paper_authors.avatarUrl: /avatars/3e46067979a4772ed533f49c3eeb1bec.svg
paper_authors.fullname: Lukas Stankevičius
paper_authors.type: user
paper_authors.user: LukasStankevicius
paper_title: Sentiment Analysis of Lithuanian Online Reviews Using Large Language
    Models
paper_summary: 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.
paper_ai_keywords: transformer models pre-trained multilingual Large Language Models (LLMs) BERT T5 fine-tuning sentiment analysis five-star-based online reviews