scholarly journals Will I Remain Popular? A Study Case on Spotify

2019 ◽  
Author(s):  
Carlos Araujo ◽  
Marco Cristo ◽  
Rafael Giusti
Keyword(s):  
Support Vector Machine ◽  
Support Vector ◽  
Popular Song ◽  
Linear Kernel ◽  
Financial Return ◽  
Music Consumption ◽  
Study Case ◽  
Streaming Service ◽  
Online Streaming ◽  
Important Form

Online streaming platforms are now the most important form of music consumption. In this paper, we present a model for predicting if a popular song on Spotify will remain popular after a certain amount of time. Spotify is the second biggest global streaming service. If a song is popular on this plataform it will ensure a good financial return for the artist and his label. We approach the problem as a classification task and employ classificators built on past information from the plataform's Top 50 Global ranking. The Support Vector Machine with linear kernel classificator reached the best results. We also verify if acoustic information can provide useful features for this problem.We made a series of classication rounds, where the results of one round were used as input of posterior rounds. Our results show that rankings previous data alone is sufficient to predict if a song will remain at the Top 50 Global two months in advance, achieving accuracy, negative predictive value, recall, specificity and F1 Score higher than 70\% for this task.

scholarly journals Predicting Music Popularity on Streaming Platforms

2019 ◽  
Author(s):  
Carlos Soares Araujo ◽  
Marco Cristo ◽  
Rafael Giusti
Keyword(s):  
Support Vector Machine ◽  
Public Interest ◽  
Support Vector Machine Model ◽  
Classification Task ◽  
Support Vector ◽  
Popular Song ◽  
Machine Model ◽  
Music Consumption ◽  
Online Streaming

Online streaming platforms have become one of the most important forms of music consumption. Most streaming platforms provide tools to assess the popularity of a song in the forms of scores and rankings. In this paper, we address two issues related to song popularity. First, we predict whether an already popular song may attract higher-than-average public interest and become “viral”. Second, we predict whether sudden spikes in public interest will translate into long-term popularity growth. We base our findings in data from the streaming platform Spotify and consider appearances in its “Most-Popular” list as indicative of popularity, and appearances in its “Virals” list as indicative of interest growth. We approach the problem as a classification task and employ a Support Vector Machine model built on popularity information to predict interest, and vice versa. We also verify if acoustic information can provide useful features for both tasks. Our results show that the popularity information alone is sufficient to predict future interest growth, achieving a F1-score above 90% at predicting whether a song will be featured in the “Virals” list after being observed in the “Most-Popular”.

scholarly journals Bankruptcy Prediction and Stress Quantification Using Support Vector Machine: Evidence from Indian Banks

Risks ◽  
2020 ◽  
Vol 8 (2) ◽  
pp. 52
Author(s):  
Santosh Kumar Shrivastav ◽  
P. Janaki Ramudu
Keyword(s):  
Support Vector Machine ◽  
Financial Stress ◽  
Vital Role ◽  
Bankruptcy Prediction ◽  
Support Vector ◽  
Decision Boundary ◽  
Linear Kernel ◽  
Research Activity ◽  
Feature Selection Technique ◽  
Private And Public

Banks play a vital role in strengthening the financial system of a country; hence, their survival is decisive for the stability of national economies. Therefore, analyzing the survival probability of the banks is an essential and continuing research activity. However, the current literature available indicates that research is currently limited on banks’ stress quantification in countries like India where there have been fewer failed banks. The literature also indicates a lack of scientific and quantitative approaches that can be used to predict bank survival and failure probabilities. Against this backdrop, the present study attempts to establish a bankruptcy prediction model using a machine learning approach and to compute and compare the financial stress that the banks face. The study uses the data of failed and surviving private and public sector banks in India for the period January 2000 through December 2017. The explanatory features of bank failure are chosen by using a two-step feature selection technique. First, a relief algorithm is used for primary screening of useful features, and in the second step, important features are fed into the support vector machine to create a forecasting model. The threshold values of the features for the decision boundary which separates failed banks from survival banks are calculated using the decision boundary of the support vector machine with a linear kernel. The results reveal, inter alia, that support vector machine with linear kernel shows 92.86% forecasting accuracy, while a support vector machine with radial basis function kernel shows 71.43% accuracy. The study helps to carry out comparative analyses of financial stress of the banks and has significant implications for their decisions of various stakeholders such as shareholders, management of the banks, analysts, and policymakers.

scholarly journals Effects of kernels and the proportion of training data on the accuracy of SVM sentiment analysis in lecturer evaluation

2020 ◽  
Vol 9 (4) ◽  
pp. 734
Author(s):  
Daniel Febrian Sengkey ◽  
Agustinus Jacobus ◽  
Fabian Johanes Manoppo
Keyword(s):  
Support Vector Machine ◽  
Sentiment Analysis ◽  
Statistical Methods ◽  
Statistical Test ◽  
The Other ◽  
Training Data ◽  
Support Vector ◽  
Linear Kernel ◽  
Linear Polynomial ◽  
Accuracy Data

Support vector machine (SVM) is a known method for supervised learning in sentiment analysis and there are many studies about the use of SVM in classifying the sentiments in lecturer evaluation. SVM has various parameters that can be tuned and kernels that can be chosen to improve the classifier accuracy. However, not all options have been explored. Therefore, in this study we compared the four SVM kernels: radial, linear, polynomial, and sigmoid, to discover how each kernel influences the accuracy of the classifier. To make a proper assessment, we used our labeled dataset of students’ evaluations toward the lecturer. The dataset was split, one for training the classifier, and another one for testing the model. As an addition, we also used several different ratios of the training:testing dataset. The split ratios are 0.5 to 0.95, with the increment factor of 0.05. The dataset was split randomly, hence the splitting-training-testing processes were repeated 1,000 times for each kernel and splitting ratio. Therefore, at the end of the experiment, we got 40,000 accuracy data. Later, we applied statistical methods to see whether the differences are significant. Based on the statistical test, we found that in this particular case, the linear kernel significantly has higher accuracy compared to the other kernels. However, there is a tradeoff, where the results are getting more varied with a higher proportion of data used for training.

scholarly journals Modeling Student’s Academic Performance During Covid-19 Based on Classification in Support Vector Machine

2021 ◽  
Vol 12 (5) ◽  
pp. 1798-1804
Author(s):  
Nor Ain Maisarah Samsudin, Et. al.
Keyword(s):  
Support Vector Machine ◽  
Academic Performance ◽  
Undergraduate Students ◽  
Movement Control ◽  
Polynomial Kernel ◽  
Support Vector ◽  
Linear Kernel ◽  
Grade Point ◽  
Before And After ◽  
And Mathematics

This study proposed a statistical investigate the pattern of students’ academic performance before and after online learning due to the Movement Control Order (MCO) during pandemic outbreak and a modelling students’ academic performance based on classification in Support Vector Machine (SVM). Data sample were taken from undergraduate students of Faculty of Science and Mathematics, Universiti Pendidikan Sultan Idris (UPSI). Student’s Grade Point Average (GPA) were obtained to developed model of academic performances during Covid-19 outbreak. The prediction model was used to predict the academic performances of university students when online classes was conducted. The algorithm of Support Vector Machine (SVM) was used to develop a model of students’ academic performance in university. For the Support Vector Machine (SVM) algorithm, there are two important parameters which are C (misclassification tolerance parameter) and epsilon  need to identify before proceed the further analysis. The parameters was applied to four different types of kernel which is linear kernel, radial basis function kernel, polynomial kernel and sigmoid kernel and the result was found that the best accuracy achieved by SVM are 73.68% by using linear kernel and the worst accuracy obtained from a sigmoid kernel which is 67.99% with parameter of misclassification tolerance C is 128 and epsilon is 0.6.

Quantitative identification of coal texture using the support vector machine with geophysical logging data: A case study using medium-rank coal from the Panjiang, Guizhou, China

2020 ◽  
Vol 8 (4) ◽  
pp. T753-T762
Author(s):  
Zhenghui Xiao ◽  
Wei Jiang ◽  
Bin Sun ◽  
Yunjiang Cao ◽  
Lei Jiang ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Texture Analysis ◽  
Quantitative Methods ◽  
Hard Coal ◽  
Support Vector ◽  
Linear Kernel ◽  
Accuracy Rate ◽  
Geophysical Logging ◽  
Soft Coal ◽  
Natural Gamma

Coal texture is important for predicting coal seam permeability and selecting favorable blocks for coalbed methane (CBM) exploration. Drilled cores and mining seam observations are the most direct and effective methods of identifying coal texture; however, they are expensive and cannot be used in unexplored coal seams. Geophysical logging has become a common method of coal texture identification, particularly during the CBM mining stage. However, quantitative methods for identifying coal texture based on geophysical logging data require further study. The support vector machine (SVM), a machine-learning method, has received great interest due to its remarkable generalization performance, and it has been used to quantitatively identify hard and soft coal using geophysical logging data. In this study, four well-logging curves, the acoustic time difference (AC), caliper log (CAL), density (DEN), and natural gamma (GR), were used for coal texture analysis. Hard coal (undeformed and cataclastic coal) exhibited higher DEN, GR, lower CAL, and lower AC than soft coal. The accuracy rate of coal texture identification was highest (97%) when the linear kernel function was applied, and the maximum training accuracy rate was achieved when the penalty parameter value of the linear kernel increased to 1. The results of verification with a newly cored CBM exploration well indicated that the SVM-based identification method was effective for coal texture analysis. With the increasing availability of data, this method can be used to distinguish hard and soft coal in a coal-bearing basin under numerous sample learning conditions.

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