用大模型分析孟加拉电商评论情感，准确率达95.5%
LLM Based Sentiment Classification From Bangladesh E-Commerce Reviews
- 基于Llama-3.1-8B微调，结合LoRA高效参数更新
- 在4000条孟加拉语与英语评论上达到95.5%准确率
- 适合资源有限环境下部署的轻量化情感分析应用
情感分析是文本分析的重要组成部分，用于识别和评估作者的情绪状态。该方法有助于全面理解消费者的情感、观点和偏好。大型语言模型（如Llama）的出现显著提升了情感分析等前沿应用的可用性。然而，书面语言的复杂性和语言多样性仍制约着分析精度。本文研究了基于Transformer的BERT模型及其他大模型在孟加拉电商评论情感分析中的适用性。使用原始数据集中4000条孟加拉语与英语用户评论进行微调，结果表明，经微调的Llama-3.1-8B模型在整体准确率、精确率、召回率和F1分数上分别达到95.5%、93%、88%、90%，优于Phi-3.5-mini-instruct、Mistral-7B-v0.1、DistilBERT-multilingual、mBERT和XLM-R-base等模型。研究强调了低参数量微调方法（如LoRA和PEFT）可降低计算开销，适用于资源受限场景。
原文摘要 · Abstract (English)
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
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