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Author Profiling (AP) aims at predicting specific characteristics from a group of authors by analyzing their written documents. Many research has been focused on determining suitable features for modeling writing patterns from authors. Reported results indicate that content-based features continue to be the most relevant and discriminant features for solving this task. Thus, in this paper, we present a thorough analysis regarding the appropriateness of different distributional term representations (DTR) for the AP task. In this regard, we introduce a novel framework for supervised AP using these representations and, supported on it. We approach a comparative analysis of representations such as DOR, TCOR, SSR, and word2vec in the AP problem. We also compare the performance of the DTRs against classic approaches including popular topic-based methods. The obtained results indicate that DTRs are suitable for solving the AP task in social media domains as they achieve competitive results while providing meaningful interpretability. [ABSTRACT FROM AUTHOR]

The Author Profiling (AP) task aims to reveal as much as possible information from a given author’s document (e.g., age, gender, etc.). AP is crucial for several applications, ranging from customized advertising to computer forensics, psychology, and entertainment. Nonetheless, the AP task is far from being solved, particularly in social media domains, where the nature of documents hinder the applicability of state-of-the-art text mining tools (e.g., because of spelling-grammar errors, huge vocabularies, and the presence of many out-of-vocabulary terms). Currently, most of the work in AP for social media has been devoted to the development of descriptive features, which are used under standard representations, such as the Bag-of-Words (BoW). Nevertheless, BoW-like representations have some well known shortcomings, namely: (i) the sparsity and high dimensionality of the representation, and (ii) the failure to capture relationships, other than mere occurrence, among terms. This paper focuses on the study of alternative document representations that can deal with such issues. We propose a representation for documents that capture discriminative and subprofile-specific information of terms. Under the proposed representation, terms are represented in a vector space that captures discriminative information. Then, term representations are aggregated to represent the content of a document. In this manner, documents are represented in a low-dimensional (and discriminative) space which is non-sparse. We evaluate the effectiveness of the proposed representation on several corpora from the social media domain. The proposed representation is compared to the standard BoW representation and a wide variety of state-of-the-art AP approaches. Experimental results reveal that the proposed representation outperforms most of the reference methodologies. Furthermore, we show that the proposed representation is in agreement with previous studies on handcrafted attributes for AP. [ABSTRACT FROM AUTHOR]

The Author Profiling (AP) task aims to predict specific profile characteristics of authors by analyzing their written documents. Nowadays, its relevance has been highlighted thanks to several applications in computer forensics, security and marketing. Most previous contributions in AP have been devoted to determine a suitable set of features to model the writing profile of authors. However, in social media this task is challenging due to the informal communication. In this regard, we present a novel approach, which considers that terms located in phrases exposing personal information have a special value for discriminating the author’s profile. The aim of this research work is to emphasize the value of such personal phrases by means of two new proposals: a feature selection method and term weighting scheme, both based on a novel measure called Personal Expression Intensity (PEI) which scores the quantity of personal information revealed by a term. For evaluating the latter ideas, we show experimental results in age and gender prediction of media users on six different collections. Average improvements of 7.34% and 5.76% for age and gender classification were obtained when comparing to the best result from state-of-the-art, indicating that personal phrases play a key role for the AP task by means of selecting and weighting terms. [ABSTRACT FROM AUTHOR]