研究人员比较了传统机器学习方法与深度学习模型在各种自然语言处理任务中的表现，包括细粒度情感分类和情感分析。研究使用了20种情感文本分类数据集和印度尼西亚电子商务评论等数据集。研究结果普遍表明，深度学习模型，特别是双向长短期记忆（BiLSTM）网络，通过更好地捕捉文本中的上下文细微差别，通常能获得更优越的性能。然而，传统的机器学习方法，如支持向量机和逻辑回归，在准确性方面仍然具有竞争力，并且在某些数据集上提供更高的计算效率。 AI
影响 强调了深度学习的性能与传统机器学习在NLP任务中的效率之间的权衡。
排序理由 该集群包含多篇学术论文，比较了不同的机器学习和深度学习方法在NLP任务中的应用。
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arXiv:2604.26310v1 Announce Type: new Abstract: Fine-grained emotion classification, which identifies specific emotional states such as happiness, anger, sadness, and fear, remains a challenging task in natural language processing. This study benchmarks classical machine learning…
Fine-grained emotion classification, which identifies specific emotional states such as happiness, anger, sadness, and fear, remains a challenging task in natural language processing. This study benchmarks classical machine learning and deep learning approaches for 20-class emoti…
arXiv:2604.25392v1 Announce Type: new Abstract: This paper benchmarks a classical machine learning approach based on PyCaret AutoML against a deep learning approach based on IndoBERT fine-tuning for binary sentiment analysis of Indonesian-language Twitter comments related to Ibu …
arXiv:2604.25452v1 Announce Type: new Abstract: Sentiment analysis of product reviews on e-commerce platforms plays a critical role in automatically understanding customer satisfaction and providing actionable insights for sellers seeking to improve product quality. This paper pr…
Sentiment analysis of product reviews on e-commerce platforms plays a critical role in automatically understanding customer satisfaction and providing actionable insights for sellers seeking to improve product quality. This paper presents a comprehensive benchmarking study compar…
This paper benchmarks a classical machine learning approach based on PyCaret AutoML against a deep learning approach based on IndoBERT fine-tuning for binary sentiment analysis of Indonesian-language Twitter comments related to Ibu Kota Nusantara (IKN). The dataset contains 1,472…
arXiv:2604.24720v1 Announce Type: new Abstract: Indonesian marketplace reviews mix standard vocabulary with slang, regional loanwords, numeric shorthands, and emoji, making lexicon-based sentiment tools unreliable in practice. This paper describes a two-track classification pipel…
Indonesian marketplace reviews mix standard vocabulary with slang, regional loanwords, numeric shorthands, and emoji, making lexicon-based sentiment tools unreliable in practice. This paper describes a two-track classification pipeline applied to the PRDECT-ID dataset, which cont…