Implementation of Support Vector Machine Algorithm with Correlation-Based Feature Selection and Term Frequency Inverse Document Frequency for Sentiment Analysis Review Hotel

  • Ririanti N
  • Purwinarko A
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Abstract

Purpose: The study aims to reduce the number of irrelevant features in sentiment analysis with large features. Methods/Study design/approach: The Support Vector Machine (SVM) algorithm is used to classify hotel review sentiment analysis because it has advantages in processing large datasets. Term Frequency-Inverse Document Frequency (TF-IDF) is used to give weight values to features in the dataset. Result/Findings: This study's results indicate that the accuracy of the SVM method with TF-IDF produces an accuracy of 93.14%, and the SVM method in the classification of hotel reviews by implementing TFIDF and CFS has increased by 1.18% from 93.14% to 94.32%. Novelty/Originality/Value: Use of Correlation-Based Feature Section (CFS) for the feature selection process, which reduces the number of irrelevant features by ranking the feature subset based on the strong correlation value in each feature

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APA

Ririanti, N. P., & Purwinarko, A. (2021). Implementation of Support Vector Machine Algorithm with Correlation-Based Feature Selection and Term Frequency Inverse Document Frequency for Sentiment Analysis Review Hotel. Scientific Journal of Informatics, 8(2), 297–303. https://doi.org/10.15294/sji.v8i2.29992

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