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DOI: 10.14569/IJACSA.2019.0100309
PDF

Optimizing the Hyperparameter of Feature Extraction and Machine Learning Classification Algorithms

Author 1: Sani Muhammad Isa
Author 2: Rizaldi Suwandi
Author 3: Yosefina Pricilia Andrean

International Journal of Advanced Computer Science and Applications(IJACSA), Volume 10 Issue 3, 2019.

  • Abstract and Keywords
  • How to Cite this Article
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Abstract: The process of assigning a quantitative value to a piece of text expressing a mood or effect is called Sentiment analysis. Comparison of several machine learning, feature extraction approaches, and parameter optimization was done to achieve the best accuracy. This paper proposes an approach to extracting comparison value of sentiment review using three features extraction: Word2vec, Doc2vec, Terms Frequency-Inverse Document Frequency (TF-IDF) with machine learning classification algorithms, such as Support Vector Machine (SVM), Naive Bayes and Decision Tree. Grid search algorithm is used to optimize the feature extraction and classifier parameter. The performance of these classification algorithms is evaluated based on accuracy. The approach that is used in this research succeeded to increase the classification accuracy for all feature extractions and classifiers using grid search hyperparameter optimization on varied pre-processed data.

Keywords: Sentiment analysis; word2vec; TF-IDF (terms frequency-inverse document frequency); Doc2vec; grid search

Sani Muhammad Isa, Rizaldi Suwandi and Yosefina Pricilia Andrean, “Optimizing the Hyperparameter of Feature Extraction and Machine Learning Classification Algorithms” International Journal of Advanced Computer Science and Applications(IJACSA), 10(3), 2019. http://dx.doi.org/10.14569/IJACSA.2019.0100309

@article{Isa2019,
title = {Optimizing the Hyperparameter of Feature Extraction and Machine Learning Classification Algorithms},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2019.0100309},
url = {http://dx.doi.org/10.14569/IJACSA.2019.0100309},
year = {2019},
publisher = {The Science and Information Organization},
volume = {10},
number = {3},
author = {Sani Muhammad Isa and Rizaldi Suwandi and Yosefina Pricilia Andrean}
}



Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

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