Hariguna Taqwa, Ruangkanjanases Athapol
Information Systems, Universitas Amikom Purwokerto, Purwokerto, Jawa Tengah, Indonesia.
Department of Commerce Chulalongkorn Business School, Chulalongkorn University, Bangkok, Thailand.
PeerJ Comput Sci. 2023 May 9;9:e1378. doi: 10.7717/peerj-cs.1378. eCollection 2023.
The primary objective of this research is to create a multi-output classification model for sentiment analysis through the combination of 10 algorithms: BernoulliNB, Decision Tree, K-nearest neighbor, Logistic Regression, LinearSVC, Bagging, Stacking, Random Forest, AdaBoost, and ExtraTrees. In doing so, we aim to identify the optimal algorithm performance and role within the model. The data utilized in this study is derived from customer reviews of cryptocurrencies in Indonesia. Our results indicate that LinearSVC and Stacking exhibit a high accuracy (90%) compared to the other eight algorithms. The resulting multi-output model demonstrates an average accuracy of 88%, which can be considered satisfactory. This research endeavors to innovate in adaptive sentiment analysis classification by developing a multi-output model that utilizes a combination of 10 classification algorithms.
本研究的主要目标是通过结合10种算法创建一个用于情感分析的多输出分类模型：伯努利朴素贝叶斯、决策树、K近邻、逻辑回归、线性支持向量分类器、装袋法、堆叠法、随机森林、自适应增强和极端随机树。通过这样做，我们旨在确定模型内最优的算法性能和作用。本研究中使用的数据来自印度尼西亚加密货币的客户评论。我们的结果表明，与其他八种算法相比，线性支持向量分类器和堆叠法表现出较高的准确率（90%）。所得的多输出模型显示平均准确率为88%，这可以认为是令人满意的。本研究致力于通过开发一个利用10种分类算法组合的多输出模型，在自适应情感分析分类方面进行创新。
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