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Adaptive sentiment analysis using multioutput classification: a performance comparison
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DOI：10.7717/peerj-cs.1378.png)
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Abstract
En 中文
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 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.
Keywords:
Cryptocurrency BernoulliNB
Decision Tree
K-nearest neighbor
Logistic Regression
LinearSVC
Bagging and Stacking
Random Forest
AdaBoost and ExtraTrees
Comparation
Multioutput
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