Assessment Measures That Discriminate between Levels of DUI Clients

1985 ◽  
Vol 56 (3) ◽  
pp. 739-742 ◽  
Author(s):  
Nina W. Brown
Keyword(s):  
Cross Validation ◽  
Profile Of Mood States ◽  
Action Program ◽  
Test Results ◽  
Multiple Discriminant Analysis ◽  
Adjective Checklist ◽  
Combined Test ◽  
Assessment Measures ◽  
Affect Adjective ◽  
Safety Action

The Adjective Checklist, Multiple Affect Adjective Checklist, Depression Adjective Checklist, and Profile of Mood States were administered to 166 males enrolled in an Alcohol Safety Action Program. Although a few individual scales showed significant differences between groups, a stepwise multiple discriminant analysis of combined test scales identified two functions, Intrapersonal Attributes which accounted for 84% of the variance and Personal Adequacy, accounting for 15.9% of the variance. The combined test results correctly classified 62% of the clients. Cross-validation is planned.

Analysis of first-trimester combined test results in preparation for a cell-free fetal DNA era

2016 ◽  
Vol 135 (2) ◽  
pp. 187-191
Author(s):  
Semir Kose ◽  
Dilek Cımrın ◽  
Nuri Yıldırım ◽  
Ozge Aksel ◽  
Pembe Keskinoglu ◽  
...  
Keyword(s):  
First Trimester ◽  
Test Results ◽  
Fetal Dna ◽  
Cell Free Fetal Dna ◽  
Combined Test ◽  
A Cell

scholarly journals ANALISIS SENTIMEN GOJEK PADA MEDIA SOSIAL TWITTER DENGAN KLASIFIKASI SUPPORT VECTOR MACHINE (SVM

2020 ◽  
Vol 9 (3) ◽  
pp. 376-390
Author(s):  
Nur Fitriyah ◽  
Budi Warsito ◽  
Di Asih I Maruddani
Keyword(s):  
Support Vector Machine ◽  
Cross Validation ◽  
Classification Model ◽  
Support Vector ◽  
Test Results ◽  
Machine Method ◽  
Support Vector Machine Method ◽  
Rbf Kernel ◽  
Negative Sentiment ◽  
Fold Cross Validation

Appearance of PT Aplikasi Karya Anak Bangsa or as known as Gojek since 2015 give a convenience facility to people in Indonesia especially in daily activities. Sentiment analysis on Twitter social media can be the option to see how Gojek users respond to the services that have been provided. The response was classified into positive sentiment and negative sentiment using Support Vector Machine method with model evaluation 10-fold cross validation. The kernel used is the linear kernel and the RBF kernel. Data labeling can be done with manually and sentiment scoring. The test results showed that the RBF kernel gets overall accuracy and the highest kappa accuracy on manual data labeling and sentiment scoring. On manual data labeling, the overall accuracy is 79.19% and kappa accuracy is 16.52%. While the labeling of data with sentiment scoring obtained overall accuracy of 79.19% and kappa accuracy of 21%. The greater overall accuracy value and kappa accuracy obtained, the better performance of the classification model. Keywords: Gojek, Twitter, Support Vector Machine, overall accuracy, kappa accuracy

Depressive and Elative Mood Inductions as a Function of Exaggerated versus Contradictory Facial Expressions

1989 ◽  
Vol 68 (2) ◽  
pp. 443-452 ◽  
Author(s):  
Patricia T. Riccelli ◽  
Carol E. Antila ◽  
J. Alexander Dale ◽  
Herbert L. Klions
Keyword(s):  
Facial Expression ◽  
Facial Expressions ◽  
Scoring System ◽  
Mood Induction ◽  
The Other ◽  
Facial Action ◽  
Adjective Checklist ◽  
Visual Scoring ◽  
Induction Sequence ◽  
Affect Adjective

Two studies concerned the relation between facial expression cognitive induction of mood and perception of mood in women undergraduates. In Exp. 1, 20 subjects were randomly assigned to a group who were instructed in exaggerated facial expressions (Demand Group) and 20 subjects were randomly assigned to a group who were not instructed (Nondemand Group). All subjects completed a modified Velten (1968) elation- and depression-induction sequence. Ratings of depression on the Multiple Affect Adjective Checklist increased during the depression condition and decreased during the elation condition. Subjects made more facial expressions in the Demand Group than the Nondemand Group from electromyogram measures of the zygomatic and corrugator muscles and from corresponding action unit measures from visual scoring using the Facial Action Scoring System. Subjects who were instructed in the Demand Group rated their depression as more severe during the depression slides than the other group. No such effect was noted during the elation condition. In Exp. 2, 16 women were randomly assigned to a group who were instructed in facial expressions contradictory to those expected on the depression and elation tasks (Contradictory Expression Group). Another 16 women were randomly assigned to a group who were given no instructions about facial expressions (Nondemand Group). All subjects completed the depression- and elation-induction sequence mentioned in Exp. 1. No differences were reported between groups on the ratings of depression (MAACL) for the depression-induction or for the elation-induction but both groups rated depression higher after the depression condition and lower after the elation condition. Electromyographic and facial action scores verified that subjects in the Contradictory Expression Group were making the requested contradictory facial expressions during the mood-induction sequences. It was concluded that the primary influence on emotion came from the cognitive mood-induction sequences. Facial expressions only seem to modify the emotion in the case of depression being exacerbated by frowning. A contradictory facial expression did not affect the rating of an emotion.

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