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The verbal protocols of college students suggest that Snapchat is an ideal social media for developing acquaintance brands: brands that aim to make themselves part of an inclusive, feel-good experience or highly relatable acquaintances.
We propose a new version of the SVM , called the scaled SVM, which can further reduce the mean of the generalization error of the SVM.
This study highlights the interpersonal value of Snapchat and adds to the existing literature examining various information and communication technologies (ICTs) as tools for relational maintenance.
The selection of SVM parameters has an important influence on the classification accuracy of SVM.
The results of experiments show that grid search-based SVM outperforms other optimized SVM approaches with 88.0% accuracy.
Proceedings ArticleDOI
Erik M. Ferragut, Jason A. Laska 
12 Dec 2012
13 Citations
Experiments on several datasets show that our method is faster than and comparably accurate to both the original SVM algorithm it is based on and the Cascade SVM, the leading data organization approach for SVMs in the literature.
Proceedings ArticleDOI
13 Dec 2007
16 Citations
Experimental results show that the performance of the proposed SVM-based method is much better than the basic SVM classifier.

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Why kernel parameter is suited for SVM sentiment analysis?4 answersKernel parameters are important for SVM sentiment analysis because they play a crucial role in improving the performance of the SVM algorithm. Different kernel functions can be used in SVM, such as Polynomial, Radial Basis Function (RBF), Sigmoid, and Linear kernels. The choice of the suitable kernel function depends on the specific application of SVM. In the context of sentiment analysis, several papers have compared the performance of different kernel functions for classifying sentiments in various domains. For example,found that the performance of the Polynomial kernel was slightly higher than the RBF kernel for sentiment analysis of online product reviews. Similarly,compared four kernel functions (Polynomial, Sigmoid, Linear, and RBF) and found that the RBF kernel achieved the best performance in terms of accuracy, precision, recall, and f-measure for sentiment analysis of tweets related to the omnibus law in Indonesia. Therefore, the choice of the kernel parameter is crucial for achieving optimal performance in SVM sentiment analysis.
What parameter is suited for SVM sentiment analysis?4 answersThe performance of SVM for sentiment analysis varies depending on the kernel function used. In the context of sentiment analysis, two commonly used kernel functions for SVM are Polynomial and Radial Basis Function (RBF). In general, the performance of the Polynomial kernel is slightly higher than that of the RBF kernel. However, recent studies have explored other variations of SVM, such as twin objective function SVMs, which have shown superior accuracy and stability compared to traditional SVMs. Additionally, the optimization of SVM parameters using techniques like the Shark smell optimizer algorithm can significantly improve the accuracy of the model. Therefore, the choice of the most suitable parameter for SVM sentiment analysis depends on the specific requirements and goals of the analysis.
What are the most important SVM parameters for sentiment analysis?4 answersThe most important SVM parameters for sentiment analysis are the kernel function and the tuning of the SVM model. The choice of kernel function, such as Polynomial or Radial Basis Function (RBF), plays a crucial role in classifying sentiment in product reviews. Additionally, tuning the SVM model using optimization techniques like the Shark smell optimizer algorithm can significantly impact the accuracy of the sentiment analysis model. The optimization process helps in selecting the best values for SVM parameters, leading to improved overall model performance. Therefore, the selection of an appropriate kernel function and the optimization of SVM parameters are key factors in achieving accurate sentiment analysis results.
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