孟加拉语情感分析的研究进展:基于变压器和迁移学习的电子商务情感分类模型的比较研究

Zishan Ahmed, Shakib Sadat Shanto, Akinul Islam Jony
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引用次数: 0

摘要

背景:互联网发展的直接结果是,互联网用户在其众多平台上共享的信息量增加了。当有更多可用的观点和意见时,情感分析在更高层次上起作用。然而,缺乏标记数据使使用孟加拉语自然语言处理(NLP)的情感分析变得非常复杂。然而,近年来,由于更有效的深度学习模型的发展,孟加拉语情感分析有了显着改善。目的:本文提供了一个仅从“Daraz”平台获得的用于孟加拉电子商务情感分析的精选数据集。我们的目标是在孟加拉语中对二元和未充分研究的多类分类任务进行情感分析。方法:在我们的数据集上比较迁移学习(LSTM, GRU)和变形金刚(Bangla-BERT)方法的有效性。为了提高模型的整体性能,我们对它们进行了微调。结果:Bangla-BERT在二元和多类情感分类任务中准确率最高,二元分类准确率为94.5%,多类情感分类准确率为88.78%。结论:与之前的深度学习技术相比,我们提出的方法在分类孟加拉语的多类情绪方面表现明显更好。关键词:Bangla-BERT,深度学习,电子商务,NLP,情感分析
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Advancement in Bangla Sentiment Analysis: A Comparative Study of Transformer-Based and Transfer Learning Models for E-commerce Sentiment Classification
Background: As a direct result of the Internet's expansion, the quantity of information shared by Internet users across its numerous platforms has increased. Sentiment analysis functions at a higher level when there are more available perspectives and opinions. However, the lack of labeled data significantly complicates sentiment analysis utilizing Bangla natural language processing (NLP). In recent years, nevertheless, due to the development of more effective deep learning models, Bangla sentiment analysis has improved significantly. Objective: This article presents a curated dataset for Bangla e-commerce sentiment analysis obtained solely from the "Daraz" platform. We aim to conduct sentiment analysis in Bangla for binary and understudied multiclass classification tasks. Methods: Transfer learning (LSTM, GRU) and Transformers (Bangla-BERT) approaches are compared for their effectiveness on our dataset. To enhance the overall performance of the models, we fine-tuned them. Results: The accuracy of Bangla-BERT was highest for both binary and multiclass sentiment classification tasks, with 94.5% accuracy for binary classification and 88.78% accuracy for multiclass sentiment classification. Conclusion: Our proposed method performs noticeably better classifying multiclass sentiments in Bangla than previous deep learning techniques. Keywords: Bangla-BERT, Deep Learning, E-commerce, NLP, Sentiment Analysis
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