NDLI: Feature Analysis for Computational Personality Recognition Using YouTube Personality Data set

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Feature Analysis for Computational Personality Recognition Using YouTube Personality Data set

Content Provider	ACM Digital Library
Author	Sarkar, Chandrima Li, Juan Agarwal, Arvind Bhatia, Sumit
Abstract	It is an important yet challenging task to develop an intelligent system in a way that it automatically classifies human personality traits. Automatic classification of human traits requires the knowledge of significant attributes and features that contribute to the prediction of a given trait. Motivated by the fact that detection of significant features is an essential part of a personality recognition system, we present in this paper an in-depth analysis of audio visual, text, demographic and sentiment features for classification of multi-modal personality traits namely, extraversion, agreeableness, conscientiousness, emotional stability and openness to experience. We use the YouTube personality data set and use logistic regression model with a ridge estimator for the classification purpose. We experiment with audio-visual features, bag of word features, sentiment based and demographic features. Our results provide important insights about the significance of different feature types for personality classification task.
Starting Page	11
Ending Page	14
Page Count	4
File Format	PDF
ISBN	9781450331296
DOI	10.1145/2659522.2659528
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2014-11-07
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Sentiment analysis Feature selection Classification
Content Type	Text
Resource Type	Article

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