scholarly journals Combining support vector machine with radial basis function kernel and information gain for sentiment analysis of movie reviews

2021 ◽  
Vol 1918 (4) ◽  
pp. 042157
Author(s):  
Z Abidin ◽  
W Destian ◽  
R Umer
Keyword(s):  
Support Vector Machine ◽  
Radial Basis Function ◽  
Sentiment Analysis ◽  
Basis Function ◽  
Information Gain ◽  
Support Vector ◽  
Radial Basis Function Kernel ◽  
Radial Basis

Adaptive data-driven nonlinear synchro squeezed transform with single class radial basis function kernel support vector machine applied to wind turbine planetary gearbox fault diagnostics

2019 ◽  
Vol 234 (7) ◽  
pp. 1015-1025
Author(s):  
R Uma Maheswari ◽  
R Umamaheswari
Keyword(s):  
Support Vector Machine ◽  
High Resolution ◽  
Radial Basis Function ◽  
Amplitude Modulation ◽  
Basis Function ◽  
Support Vector ◽  
Radial Basis Function Kernel ◽  
Single Class ◽  
Kernel Support Vector Machine ◽  
Radial Basis

Planetary stage gears operated at low rotational speed and varying wind speed result variation in load. Variable speed and variable load induce nonstationary operating conditions. Vibration signal measured from Wind power gear transmission systems are embedded with multiple sources of vibration and attenuated considerably as it travels from source of vibration to measuring point. Efficacious multi-component decomposition without mode mixing ensures the accurate fault signature recognition. Synchro squeezing transform is the promising tool that represents the ridges with high resolution in time as well as in frequency axis. An efficient vibration analysis technique, short windowed Fourier synchro squeezing transform with nonlinear radial basis function kernel support vector machine is proposed to detect the mechanical faults in low speed planetary stage of wind turbines. Raw vibration is modeled in time–frequency plane to extract fault pattern signatures effectively with high resolution by adapting an empirical nonlinear tool synchro squeezing transforms. Amplitude modulation and frequency modulation parameters are sculpted from instantaneous amplitude and instantaneous phase, frequency. Hybrid feature space with signal attributes, statistical moments, and randomness measures are extricated from amplitude modulation-frequency modulation components. Single class radial basis function support vector machine is trained with hybrid features. The fault detection accuracy of the proposed method is compared with the standard variants of empirical mode decomposition. The proposed short windowed Fourier synchro squeezing transform-radial basis function kernel support vector machine shows 98.2% accuracy, 98% sensitivity, and 98% specificity.

scholarly journals Sentiment Analysis Towards Kartu Prakerja Using Text Mining with Support Vector Machine and Radial Basis Function Kernel

2021 ◽  
Vol 7 (2) ◽  
pp. 119
Author(s):  
Belindha Ayu Ardhani ◽  
Nur Chamidah ◽  
Toha Saifudin
Keyword(s):  
Support Vector Machine ◽  
Text Mining ◽  
Radial Basis Function ◽  
Sentiment Analysis ◽  
Basis Function ◽  
Descriptive Analysis ◽  
Support Vector ◽  
The Future ◽  
Radial Basis ◽  
Rbf Kernel

Background: The introduction of Kartu Prakerja (Pre-employment Card) Programme, henceforth KPP, which was claimed to have launched in order to improve the quality of workforce, spurred controversy among members of the public. The discussion covered the amount of budget, the training materials and the operations brought out various reactions. Opinions could be largely divided into groups: the positive and the negative sentiments.Objective: This research aims to propose an automated sentiment analysis that focuses on KPP. The findings are expected to be useful in evaluating the services and facilities provided.Methods: In the sentiment analysis, Support Vector Machine (SVM) in text mining was used with Radial Basis Function (RBF) kernel. The data consisted of 500 tweets from July to October 2020, which were divided into two sets: 80% data for training and 20% data for testing with five-fold cross validation.Results: The results of descriptive analysis show that from the total 500 tweets, 60% were negative sentiments and 40% were positive sentiments. The classification in the testing data show that the average accuracy, sensitivity, specificity, negative sentiment prediction and positive sentiment prediction values were 85.20%; 91.68%; 75.75%; 85.03%; and 86.04%, respectively.Conclusion: The classification results show that SVM with RBF kernel performs well in the opinion classification. This method can be used to understand similar sentiment analysis in the future. In KPP case, the findings can inform the stakeholders to improve the programmes in the future. Keywords: Kartu Prakerja, Sentiment Analysis, Support Vector Machine, Text Mining, Radial Basis Function 

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