3 research outputs found
Adapting Cross-Genre Author Profiling to Language and Corpus Notebook for PAN at CLEF 2016
Abstract This paper presents our approach to the Author Profiling (AP) task at PAN 2016. The task aims at identifying the author's age and gender under crossgenre AP conditions in three languages: English, Spanish, and Dutch. Our preprocessing stage includes reducing non-textual features to their corresponding semantic classes. We exploit typed character n-grams, lexical features, and nontextual features (domain names). We experimented with various feature representations (binary, raw frequency, normalized frequency, second order attributes (SOA), tf-idf) and machine learning algorithms (liblinear and libSVM implementations of Support Vector Machines (SVM), multinomial naive Bayes, logistic regression). For textual feature selection, we applied the transition point technique, except when SOA was used. We found that the optimal configuration was different for different languages at each stage
Recent Trends in Deep Learning Based Personality Detection
Recently, the automatic prediction of personality traits has received a lot
of attention. Specifically, personality trait prediction from multimodal data
has emerged as a hot topic within the field of affective computing. In this
paper, we review significant machine learning models which have been employed
for personality detection, with an emphasis on deep learning-based methods.
This review paper provides an overview of the most popular approaches to
automated personality detection, various computational datasets, its industrial
applications, and state-of-the-art machine learning models for personality
detection with specific focus on multimodal approaches. Personality detection
is a very broad and diverse topic: this survey only focuses on computational
approaches and leaves out psychological studies on personality detection