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  • Open Access

    ARTICLE

    Enhancing Arabic Sentiment Analysis with Pre-Trained CAMeLBERT: A Case Study on Noisy Texts

    Fay Aljomah, Lama Aldhafeeri, Maha Alfadel, Sultanh Alshahrani, Qaisar Abbas*, Sarah Alhumoud*

    CMC-Computers, Materials & Continua, Vol.84, No.3, pp. 5317-5335, 2025, DOI:10.32604/cmc.2025.062478 - 30 July 2025

    Abstract Dialectal Arabic text classification (DA-TC) provides a mechanism for performing sentiment analysis on recent Arabic social media leading to many challenges owing to the natural morphology of the Arabic language and its wide range of dialect variations. The availability of annotated datasets is limited, and preprocessing of the noisy content is even more challenging, sometimes resulting in the removal of important cues of sentiment from the input. To overcome such problems, this study investigates the applicability of using transfer learning based on pre-trained transformer models to classify sentiment in Arabic texts with high accuracy. Specifically,… More >

  • Open Access

    ARTICLE

    Arabic Sentiment Analysis of Users’ Opinions of Governmental Mobile Applications

    Mohammed Hadwan1,2,3,*, Mohammed A. Al-Hagery4, Mohammed Al-Sarem5, Faisal Saeed5,6

    CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 4675-4689, 2022, DOI:10.32604/cmc.2022.027311 - 21 April 2022

    Abstract Different types of pandemics that have appeared from time to time have changed many aspects of daily life. Some governments encourage their citizens to use certain applications to help control the spread of disease and to deliver other services during lockdown. The Saudi government has launched several mobile apps to control the pandemic and have made these apps available through Google Play and the app store. A huge number of reviews are written daily by users to express their opinions, which include significant information to improve these applications. The manual processing and extracting of information… More >

  • Open Access

    ARTICLE

    Twitter Arabic Sentiment Analysis to Detect Depression Using Machine Learning

    Dhiaa A. Musleh, Taef A. Alkhales, Reem A. Almakki*, Shahad E. Alnajim, Shaden K. Almarshad, Rana S. Alhasaniah, Sumayh S. Aljameel, Abdullah A. Almuqhim

    CMC-Computers, Materials & Continua, Vol.71, No.2, pp. 3463-3477, 2022, DOI:10.32604/cmc.2022.022508 - 07 December 2021

    Abstract Depression has been a major global concern for a long time, with the disease affecting aspects of many people's daily lives, such as their moods, eating habits, and social interactions. In Arabic culture, there is a lack of awareness regarding the importance of facing and curing mental health diseases. However, people all over the world, including Arab citizens, tend to express their feelings openly on social media, especially Twitter, as it is a platform designed to enable the expression of emotions through short texts, pictures, or videos. Users are inclined to treat their Twitter accounts… More >

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