Golnoosh Farnadi

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This paper describes the submission of the University of Washington’s Center for Data Science to the PAN 2014 author profiling task. We examine the predictive quality in terms of age and gender of several sets of features extracted from various genres of online social media. Through comparison, we establish a feature set which maximizes accuracy of gender(More)
A variety of approaches have been recently proposed to automatically infer users’ personality from their user generated content in social media. Approaches differ in terms of the machine learning algorithms and the feature sets used, type of utilized footprint, and the social media environment used to collect the data. In this paper, we perform a(More)
Gaining insight in a web user’s personality is very valuable for applications that rely on personalisation, such as recommender systems and personalised advertising. In this paper we explore the use of machine learning techniques for inferring a user’s personality traits from their Facebook status updates. Even with a small set of training examples we can(More)
User generated content on social media sites is a rich source of information about latent variables of their users. Proper mining of this content provides a shortcut to emotion and personality detection of users without filling out questionnaires. This in turn increases the application potential of personalized services that rely on the knowledge of such(More)
Research in psychology has suggested that behavior of individuals can be explained to a great extent by their underlying personality traits. In this paper, we focus on predicting how the personality of YouTube video bloggers is perceived by their viewers. Our approach to personality recognition is multimodal in the sense that we use audio-video features, as(More)
Golnoosh Farnadi, Stephen H. Bach, Marie-Francine Moens, Lise Getoor, Martine De Cock Department of Applied Mathematics, Computer Science and Statistics, Ghent University, Belgium, Department of Computer Science, Katholieke Universiteit Leuven, Belgium, Statistical Relational Learning Group, University of Maryland, USA, University of California, Santa Cruz,(More)
This paper gives a brief description on the methods adopted for the task of author-profiling as part of the competition PAN 2016 [1]. Author profiling is the task of predicting the author’s age and gender from his/her writing. In this paper, we follow a two-level ensemble approach to tackle the cross-genre author profiling task where training documents and(More)
Nodes of a social graph often represent entities with specific labels, denoting properties such as age-group or gender. Design of algorithms to assign labels to unlabeled nodes by leveraging node-proximity and a-priori labels of seed nodes is of significant interest. A semi-supervised approach to solve this problem is termed "LPA-Label Propagation(More)
Quantification in statistical relational learning (SRL) is either existential or universal, however humans might be more inclined to express knowledge using soft quantifiers, such as “most” and “a few”. In this paper, we define the syntax and semantics of PSL, a new SRL framework that supports reasoning with soft quantifiers, and present its most probable(More)