الفهرس 
IntroductionOnly 14 pages are availabe for public view
 |  | from | 91 |  |  |
| | | from | 91 | | |
Abstract
Nowadays, using social media platforms like Facebook and Twitter, became a daily activity for millions of people. This extensive usage resulted in generating an enormous amount of users generated content in the form of text, images, and videos known as digital records. Analyzing this content motivated different types of applications among which, is automatic personality recognition. Automatic personality recognition is the process of predicting personality traits using users’ digital data. Recently, many approaches were proposed for the assessment of the positive traits such as the Big Five and negative traits such as the Dark Triad traits. The majority of these approaches focus on the statistical linguistic features while ignoring semantic properties extracted from the user’s text. In addition, current existing features are far away from representing social human activities.
In this thesis, an efficient approach for the prediction of the generic positive and negative personality traits using user’s generarted data on social media is proposed. Two contributions are introduced which depend on using knowledge-based semantic features and psychological personality characteristic features.
Experimental results show that when using the text semantics, the average accuracy for predicting the Big Five traits reached 60% using Information Content-based measure and 56% when using Path-based measure. For predicting the Big Five and the Dark Triad, the Logistic regression classifier using the proposed personality characteristic features recorded the best accuracy value of 80% and 70.3% respectively, followed by the Linear regression with elastic net regularizer with average accuracy equals 78.4% and 69% respectively. The proposed personality characteristic features also contributed to much lower-dimensional feature space with 80-92% savings proving better processing for predicting the traits.
For proving the existence of the relationship between the personality traits and the proposed personality characteristics features used in this work, a statistical experiment is conducted where Pearson correlation coefficients are used to measure the relationships. The experimental results proved the effectiveness of the proposed personality characteristic features for personality traits prediction.