What is the best way to select the parameters for a SVM sentiment classifier?
Best insight from top research papers
The best way to select parameters for a SVM sentiment classifier is to use the genetic algorithm to optimize the SVM model . The genetic algorithm is an optimization algorithm that can be used to select the optimal support vector subset and optimize the SVM parameters. This method has been shown to significantly improve sentiment classification compared to traditional SVM modeling capabilities . Additionally, feature selection methods such as content and syntax models can be used to automatically learn a set of features in a review document and remove entities being reviewed from subjective expressions . The selection of classification algorithms, feature selection methods, and feature dimensions can also be determined through experiments .
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| Papers (5) | Insight |
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22 Nov 2022 | The paper does not provide information on the best way to select parameters for a SVM sentiment classifier. The paper focuses on comparing classification methods using feature selection for smartphone sentiment analysis. |
22 Nov 2022 | The paper does not provide information on the best way to select parameters for a SVM sentiment classifier. The paper is about comparing classification methods using feature selection for smartphone sentiment analysis. |
04 May 2016 59 Citations | The paper does not provide information on the best way to select parameters for a SVM sentiment classifier. |
01 Aug 2016 14 Citations | The paper proposes using a genetic algorithm to optimize the parameters for a SVM sentiment classifier. |
| The paper does not provide information on the best way to select parameters for a SVM sentiment classifier. |
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