Discriminative subprofile-specific representations for author profiling in social media

López-Monroy, Adrián Pastor; Montes-y-Gómez, Manuel; Villaseñor-Pineda, Luis; Stamatatos, Efstathios

doi:10.1016/j.knosys.2015.06.024

Cited by 40 publications

(33 citation statements)

References 23 publications

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“…Binary feature representation, in all examined cases, provided higher results than term frequency (an average increase of 4.71% under 10-fold cross-validation and of 5.44% in the 50%-test setting using the balanced corpus). Therefore, in future work, alternative feature representation techniques will be tested, such as second order representation [17] or doc2vec-based feature representation [16]. The later has proved to provide good results for AA in English [24] and in related tasks [21].…”

Section: Discussionmentioning

confidence: 99%

Authorship Attribution in Portuguese Using Character N-grams

2017

APH

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For the Authorship Attribution (AA) task, character n-grams are considered among the best predictive features. In the English language, it has also been shown that some types of character n-grams perform better than others. This paper tackles the AA task in Portuguese by examining the performance of different types of character n-grams, and various combinations of them. The paper also experiments with different feature representations and machine-learning algorithms. Moreover, the paper demonstrates that the performance of the character n-gram approach can be improved by fine-tuning the feature set and by appropriately selecting the length and type of character n-grams. This relatively simple and language-independent approach to the AA task outperforms both a bag-of-words baseline and other approaches, using the same corpus.

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Section: Discussionmentioning

confidence: 99%

Authorship Attribution in Portuguese Using Character N-grams

2017

APH

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“…The user profile building process is therefore an extremely important step in order to obtain good personalized results. We can see the importance of building accurate user profiles even applied to other domains such as social media [21] or IR related fields such as recommender systems [3] .…”

Section: Related Workmentioning

confidence: 99%

Use of textual and conceptual profiles for personalized retrieval of political documents

Vicente-López

Campos

Fernández‐Luna

et al. 2016

Knowledge-Based Systems

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“…For example, Seroussi et al () report that some latent topics extracted from movie reviews and message board posts are related to the use of colloquial words, while others are related to more formal words. Topic modeling methods have already been applied to authorship analysis tasks like authorship attribution (Savoy, ; Seroussi et al, ) and author profiling (López‐Monroy, Montes‐y‐Gómez, Escalante, Villaseñor‐Pineda, & Stamatatos, ). In author verification, topic modeling has only been applied occasionally (Hernández & Calvo, ; Pacheco, Fernandes, & Porco, ; Potha & Stamatatos, ) so far.…”

Section: Introductionmentioning

confidence: 99%

Improving author verification based on topic modeling

Potha

Stamatatos

2019

Asso for Info Science & Tech

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Authorship analysis attempts to reveal information about authors of digital documents enabling applications in digital humanities, text forensics, and cyber‐security. Author verification is a fundamental task where, given a set of texts written by a certain author, we should decide whether another text is also by that author. In this article we systematically study the usefulness of topic modeling in author verification. We examine several author verification methods that cover the main paradigms, namely, intrinsic (attempt to solve a one‐class classification task) and extrinsic (attempt to solve a binary classification task) methods as well as profile‐based (all documents of known authorship are treated cumulatively) and instance‐based (each document of known authorship is treated separately) approaches combined with well‐known topic modeling methods such as Latent Semantic Indexing (LSI) and Latent Dirichlet Allocation (LDA). We use benchmark data sets and demonstrate that LDA is better combined with extrinsic methods, while the most effective intrinsic method is based on LSI. Moreover, topic modeling seems to be particularly effective for profile‐based approaches and the performance is enhanced when latent topics are extracted by an enriched set of documents. The comparison to state‐of‐the‐art methods demonstrates the great potential of the approaches presented in this study. It is also demonstrates that even when genre‐agnostic external documents are used, the proposed extrinsic models are very competitive.

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Discriminative subprofile-specific representations for author profiling in social media

Cited by 40 publications

References 23 publications

Authorship Attribution in Portuguese Using Character N-grams

Authorship Attribution in Portuguese Using Character N-grams

Use of textual and conceptual profiles for personalized retrieval of political documents

Improving author verification based on topic modeling

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