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  • Open Access

    ARTICLE

    Author’s Age and Gender Prediction on Hotel Review Using Machine Learning Techniques

    Muhammad Hood Khan1, Bilal Khan1,*, Saifullah Jan1, Muhammad Imran Chughtai2

    Journal on Big Data, Vol.5, pp. 41-56, 2023, DOI:10.32604/jbd.2022.044060

    Abstract Author’s Profile (AP) may only be displayed as an article, similar to text collection of material, and must differentiate between gender, age, education, occupation, local language, and relative personality traits. In several information-related fields, including security, forensics, and marketing, and medicine, AP prediction is a significant issue. For instance, it is important to comprehend who wrote the harassing communication. In essence, from a marketing perspective, businesses will get to know one another through examining items and websites on the internet. Accordingly, they will direct their efforts towards a certain gender or age restriction based on the kind of individuals who… More >

  • Open Access

    ARTICLE

    Novel Machine Learning–Based Approach for Arabic Text Classification Using Stylistic and Semantic Features

    Fethi Fkih1,2,*, Mohammed Alsuhaibani1, Delel Rhouma1,2, Ali Mustafa Qamar1

    CMC-Computers, Materials & Continua, Vol.75, No.3, pp. 5871-5886, 2023, DOI:10.32604/cmc.2023.035910

    Abstract Text classification is an essential task for many applications related to the Natural Language Processing domain. It can be applied in many fields, such as Information Retrieval, Knowledge Extraction, and Knowledge modeling. Even though the importance of this task, Arabic Text Classification tools still suffer from many problems and remain incapable of responding to the increasing volume of Arabic content that circulates on the web or resides in large databases. This paper introduces a novel machine learning-based approach that exclusively uses hybrid (stylistic and semantic) features. First, we clean the Arabic documents and translate them to English using translation tools.… More >

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