SVM and k-Means Hybrid Method for Textual Data Sentiment Analysis

The goal of this paper is to propose a hybrid technique to improve Support Vector Machines classification accuracy using training data sampling and hyperparameter tuning. The proposed technique applies clustering to select training data and parameter tuning to optimize classifier effectiveness. The paper reports that better results were obtained using our proposed method in all experiments, compared to results of method presented in our previous work.

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