Abstract
Sentiment analysis is an essential part of text analysis, which is a larger field that includes determining and evaluating the author's emotional state. This method is essential since it makes it easier to comprehend consumers' feelings, viewpoints, and preferences holistically. The introduction of large language models (LLMs), such as Llama, has greatly increased the availability of cutting-edge model applications, such as sentiment analysis. However, accurate sentiment analysis is hampered by the intricacy of written language and the diversity of languages used in evaluations. The viability of using transformer-based BERT models and other LLMs for sentiment analysis from Bangladesh e commerce reviews is investigated in this paper. A subset of 4000 samples from the original dataset of Bangla and English customer reviews was utilized to fine-tune the model. The fine tuned Llama-3.1-8B model outperformed other fine-tuned models, including Phi-3.5-mini-instruct, Mistral-7B-v0.1, DistilBERT-multilingual, mBERT, and XLM-R-base, with an overall accuracy, precision, recall, and F1 score of 95.5%, 93%, 88%, 90%. The study emphasizes how parameter efficient fine-tuning methods (LoRA and PEFT) can lower computational overhead and make it appropriate for contexts with limited resources. The results show how LLMs can
Abstract (translated)
情感分析是文本分析的一个重要组成部分，而文本分析则涵盖了确定和评估作者的情感状态。这种方法至关重要，因为它使得更全面地理解消费者的情绪、观点和偏好变得更容易。大型语言模型（LLMs）的引入，如Llama，极大地提高了尖端模型应用的可用性，包括情感分析。然而，书面语言的复杂性和评价中所使用语言的多样性阻碍了准确的情感分析。本文研究了在孟加拉国电子商务评论中使用基于变压器的BERT模型和其他大型语言模型进行情感分析的可能性。我们从包含孟加拉语和英语客户评论的原始数据集中选取了一部分共4000个样本，用于微调这些模型。微调后的Llama-3.1-8B模型在整体准确率、精确度、召回率以及F1分数上均优于其他微调过的模型（包括Phi-3.5-mini-instruct, Mistral-7B-v0.1, DistilBERT-multilingual, mBERT和XLM-R-base），分别达到了95.5%，93%，88%和90%。研究强调了参数高效的微调方法（如LoRA和PEFT）如何能够降低计算开销，并使之适应资源有限的环境。实验结果表明，大型语言模型可以有效应用于情感分析任务中，特别是在多语言场景下展现出强大的性能潜力。 总结： 该研究展示了在孟加拉国电子商务评论的情感分析领域使用大规模预训练模型（如Llama-3.1-8B）进行微调的有效性，并且通过实验验证了这些方法的参数效率优势。此外，结果还表明，大型语言模型能够提供更准确和全面的理解，有助于更好地服务于资源受限的情境下的应用需求。
URL
https://arxiv.org/abs/2510.01276
