Author profiling is a text classification technique, which is used to predict the profiles of unknown text by analyzing their writing styles. Author profiles are the characteristics of the authors like gender, age, nativity language, country and educational background. The existing approaches for Author Profiling suffered from problems like high dimensionality of features and fail to capture th...

This paper reports on some aspects of a project aimed at automating the analysis of texts for the purpose of author profiling and identification. The complete analysis provides probabilities for the author’s basic demographic traits (gender, age, geographic origin, level of education and native language) as well as for five psychometric traits. We describe the email data which was collected for...

Abstract This paper presents a novel author profiling method specially aimed at classifying social network users into the multidimensional perspectives for business intelligence (SBI) applications. In this scenario, being user profiles defined on demand each particular SBI application, we cannot assume existence of labelled datasets training purposes. Thus, propose an unsupervised to obtain req...

Research points to gender diff erences in individualism and collectivism (e.g., Gilligan, 1982; Kashima et al. 1995; Maccoby, 1990; Singelis, 1994). At the broadest level, women appear to be less individualistic and more collectivistic than do men (Cross & Madson, 1997; Hofstede, 2001; Markus & Kitayama, 1991; Triandis, 1995). For instance, women are more willing and able to care for others (Gi...

A new technique is introduced, linguistic profiling, in which large numbers of counts of linguistic features are used as a text profile, which can then be compared to average profiles for groups of texts. The technique proves to be quite effective for authorship verification and recogni­ tion. The best parameter settings yield a False Accept Rate of 8.1% at a False Re­ ject Rate equal to zero f...

Authorship analysis aims at classifying texts based on the stylistic choices of their authors. The idea is to discover characteristics of the authors of the texts. This task has a growing importance in forensics, security, and marketing. In this work, we focus on discovering age and gender from blog authors. With this goal in mind, we analyzed a large number of features – ranging from Informati...

This paper describes the participation of the Language and Reasoning Group of UAM at RepLab 2014 Author Profiling evaluation lab. This task involves author categorization and author ranking subtasks. Our method for author categorization uses a supervised approach based on the idea that we can use the information on Twitter’s user profile, then by means of employing an attribute selection techni...

Most natural language processing tasks deal with large amounts of data, which takes a lot of time to process. For better results, a larger dataset and a good set of features are very helpful. But larger volumes of text and high dimensionality of features will mean slower performance. Thus, natural language processing and distributed computing are a good match. In the PAN 2013 competition, the t...

This paper describes a learning system developed for the RepLab 2014 author profiling task at UNED. The system uses a voting model, which employs a small set of features based mainly on the tweet text information such as POS tags, number of hashtags or number of links. In the unofficial run, the feature set was increased with Twitter metadata such as number of followers or retweet speed. The sy...