使用多输出分类进行自适应情绪分析:一种性能比较
Taqwa Hariguna1, Athapol Ruangkanjanases2
1Information Systems, Universitas Amikom Purwokerto, Purwokerto, Jawa Tengah, Indonesia.
PeerJ. Computer science
|June 22, 2023
概括
这项研究开发了一种使用10个算法的多输出情绪分析模型,发现线性SVC和堆叠最有效. 综合模型在分析印尼加密货币评论时实现了88%的准确性.
科学领域:
- 自然语言处理自然语言处理.
- 机器学习 机器学习
- 计算语言学 计算语言学
背景情况:
- 情绪分析对于了解客户意见至关重要,特别是在像加密货币这样快速发展的市场上.
- 现有的模型可能无法完全捕捉不同数据集中的情绪细微差别,例如印度尼西亚加密货币评论.
研究的目的:
- 开发和评估一种用于情绪分析的新型多输出分类模型.
- 在一个组合模型中比较10个不同的机器学习算法的性能.
- 确定最佳算法,以分析印尼加密货币客户评论的情绪分析.
主要方法:
- 通过整合BernoulliNB,决策树,K-最近邻居,物流回归,线性SVC,袋装,堆叠,随机森林,AdaBoost和ExtraTrees,构建了一个多输出分类模型.
- 该模型是使用印尼加密货币客户评论数据集进行训练和测试的.
- 绩效是基于准确性进行评估的,重点是确定最有效的单个算法和整体合奏表现.
主要成果:
- 线性SVC和堆叠算法表现出最高的个别精度,达到90%.
- 综合多输出情绪分析模型的平均准确率为88%.
- 合奏方法在捕捉加密货币审查数据中的复杂情绪模式方面被证明是有效的.
结论:
- 开发的多输出模型为印尼加密货币市场的情绪分析提供了强大而准确的解决方案.
- 线性SVC和堆叠是这种特定情绪分析任务的高效算法.
- 这项研究通过展示各种算法组合方法的力量,为适应情绪分析做出了贡献.
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