A decision system for computational authors profiling: From machine learning to deep learning. (7th September 2020)

• Record Type: Journal Article
• Authors: Mechti, Seifeddine; Krichen, Moez; Ben Noureddine, Dhouha; Belguith, Lamia H.
• Is Part Of: Concurrency and computation. Volume 34:Number 7(2022)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2020-09-07
• Subjects: author profiling -- decision systems -- deep learning -- gated recurrent units -- machine learning; Parallel processing (Electronic computers) -- Periodicals; Parallel computers -- Periodicals; 004.35
• DOI: 10.1002/cpe.5985
• Languages: English
• ISSNs: 1532-0626

Abstract

Summary: In this study, we tackle the problem of author profiling. The aim of the proposed approach is to determine the author's age and gender. Once the user connects to the company website, this company collects the available data about him (which is usually very limited). Then, the user receives a service recommendation according to his gender and age. Thus, a context‐specific decision‐making system based on these limited data is required to produce an efficient classification. Such a decision system allows companies to promote their marketing. To obtain the best categorization, machine learning (ML) and deep learning (DL) techniques have been applied in the literature. In this article, we apply both classical ML techniques and recently developed DL techniques. More precisely, we adopt the gated recurrent unit model. Our experiments show that our findings are positively comparable with the best state‐of‐the‐art methods.