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Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
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Adaptive sentiment analysis using multioutput classification: a performance comparison.

Taqwa Hariguna1, Athapol Ruangkanjanases2

  • 1Information Systems, Universitas Amikom Purwokerto, Purwokerto, Jawa Tengah, Indonesia.

Peerj. Computer Science
|June 22, 2023
PubMed
Summary
This summary is machine-generated.

This study developed a multi-output sentiment analysis model using 10 algorithms, finding LinearSVC and Stacking most effective. The combined model achieved 88% accuracy for analyzing Indonesian cryptocurrency reviews.

Keywords:
AdaBoost and ExtraTreesBagging and StackingBernoulliNBComparationDecision TreeK-nearest neighborLinearSVCLogistic RegressionMultioutputRandom Forest

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Area of Science:

  • Natural Language Processing
  • Machine Learning
  • Computational Linguistics

Background:

  • Sentiment analysis is crucial for understanding customer opinions, especially in rapidly evolving markets like cryptocurrency.
  • Existing models may not fully capture the nuances of sentiment in diverse datasets, such as Indonesian cryptocurrency reviews.

Purpose of the Study:

  • To develop and evaluate a novel multi-output classification model for sentiment analysis.
  • To compare the performance of 10 distinct machine learning algorithms within a combined model.
  • To identify the optimal algorithms for sentiment analysis of Indonesian cryptocurrency customer reviews.

Main Methods:

  • A multi-output classification model was constructed by integrating BernoulliNB, Decision Tree, K-nearest neighbor, Logistic Regression, LinearSVC, Bagging, Stacking, Random Forest, AdaBoost, and ExtraTrees.
  • The model was trained and tested using a dataset of customer reviews for cryptocurrencies in Indonesia.
  • Performance was evaluated based on accuracy, with a focus on identifying the most effective individual algorithms and the overall ensemble performance.

Main Results:

  • LinearSVC and Stacking algorithms demonstrated the highest individual accuracy at 90%.
  • The combined multi-output sentiment analysis model achieved an average accuracy of 88%.
  • The ensemble approach proved effective in capturing complex sentiment patterns within the cryptocurrency review data.

Conclusions:

  • The developed multi-output model offers a robust and accurate solution for sentiment analysis in the Indonesian cryptocurrency market.
  • LinearSVC and Stacking are highly effective algorithms for this specific sentiment analysis task.
  • This research contributes to adaptive sentiment analysis by showcasing the power of ensemble methods with diverse algorithms.