Weisi Chen^1,*, Walayat Hussain^2,*, Junjie Chen¹

CMC-Computers, Materials & Continua, Vol.85, No.1, pp. 1559-1583, 2025, DOI:10.32604/cmc.2025.065916 - 29 August 2025

Abstract Topic modeling is a fundamental technique of content analysis in natural language processing, widely applied in domains such as social sciences and finance. In the era of digital communication, social scientists increasingly rely on large-scale social media data to explore public discourse, collective behavior, and emerging social concerns. However, traditional models like Latent Dirichlet Allocation (LDA) and neural topic models like BERTopic struggle to capture deep semantic structures in short-text datasets, especially in complex non-English languages like Chinese. This paper presents Generative Language Model Topic (GLMTopic) a novel hybrid topic modeling framework leveraging the capabilities… More >

Jungpil Shin^1,*, Wahidur Rahman², Tanvir Ahmed², Bakhtiar Mazrur², Md. Mohsin Mia², Romana Idress Ekfa², Md. Sajib Rana², Pankoo Kim^3,*

CMC-Computers, Materials & Continua, Vol.84, No.3, pp. 4105-4153, 2025, DOI:10.32604/cmc.2025.066910 - 30 July 2025

Abstract Sentiment Analysis, a significant domain within Natural Language Processing (NLP), focuses on extracting and interpreting subjective information—such as emotions, opinions, and attitudes—from textual data. With the increasing volume of user-generated content on social media and digital platforms, sentiment analysis has become essential for deriving actionable insights across various sectors. This study presents a systematic literature review of sentiment analysis methodologies, encompassing traditional machine learning algorithms, lexicon-based approaches, and recent advancements in deep learning techniques. The review follows a structured protocol comprising three phases: planning, execution, and analysis/reporting. During the execution phase, 67 peer-reviewed articles were More >

Bandar Alotaibi^1,*, Aljawhara Almutarie², Shuaa Alotaibi³, Munif Alotaibi⁴

CMC-Computers, Materials & Continua, Vol.84, No.3, pp. 4451-4467, 2025, DOI:10.32604/cmc.2025.066050 - 30 July 2025

Abstract X (formerly known as Twitter) is one of the most prominent social media platforms, enabling users to share short messages (tweets) with the public or their followers. It serves various purposes, from real-time news dissemination and political discourse to trend spotting and consumer engagement. X has emerged as a key space for understanding shifting brand perceptions, consumer preferences, and product-related sentiment in the fashion industry. However, the platform’s informal, dynamic, and context-dependent language poses substantial challenges for sentiment analysis, mainly when attempting to detect sarcasm, slang, and nuanced emotional tones. This study introduces a hybrid… More >

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 >

Ying Shen¹, Shichao Zhao¹, Yanfei Lv¹, Fei Chen¹, Li Fu^1,*, Hassan Karimi-Maleh^2,*

CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 1921-1950, 2025, DOI:10.32604/cmc.2025.067427 - 03 July 2025

Abstract This review presents a comprehensive and forward-looking analysis of how Large Language Models (LLMs) are transforming knowledge discovery in the rational design of advanced micro/nano electrocatalyst materials. Electrocatalysis is central to sustainable energy and environmental technologies, but traditional catalyst discovery is often hindered by high complexity, fragmented knowledge, and inefficiencies. LLMs, particularly those based on Transformer architectures, offer unprecedented capabilities in extracting, synthesizing, and generating scientific knowledge from vast unstructured textual corpora. This work provides the first structured synthesis of how LLMs have been leveraged across various electrocatalysis tasks, including automated information extraction from literature,… More >

Anees Ara¹, Muhammad Mujahid¹, Amal Al-Rasheed^2,*, Shaha Al-Otaibi², Tanzila Saba¹

CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 2717-2731, 2025, DOI:10.32604/cmc.2025.065566 - 03 July 2025

Abstract DeepSeek Chinese artificial intelligence (AI) open-source model, has gained a lot of attention due to its economical training and efficient inference. DeepSeek, a model trained on large-scale reinforcement learning without supervised fine-tuning as a preliminary step, demonstrates remarkable reasoning capabilities of performing a wide range of tasks. DeepSeek is a prominent AI-driven chatbot that assists individuals in learning and enhances responses by generating insightful solutions to inquiries. Users possess divergent viewpoints regarding advanced models like DeepSeek, posting both their merits and shortcomings across several social media platforms. This research presents a new framework for predicting… More >

Lilia-Eliana Popescu-Apreutesei, Mihai-Sorin Iosupescu, Sabina Cristiana Necula, Vasile-Daniel Păvăloaia^*

CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 2605-2644, 2025, DOI:10.32604/cmc.2025.064747 - 03 July 2025

Abstract The increasing fluency of advanced language models, such as GPT-3.5, GPT-4, and the recently introduced DeepSeek, challenges the ability to distinguish between human-authored and AI-generated academic writing. This situation is raising significant concerns regarding the integrity and authenticity of academic work. In light of the above, the current research evaluates the effectiveness of Bidirectional Long Short-Term Memory (BiLSTM) networks enhanced with pre-trained GloVe (Global Vectors for Word Representation) embeddings to detect AI-generated scientific abstracts drawn from the AI-GA (Artificial Intelligence Generated Abstracts) dataset. Two core BiLSTM variants were assessed: a single-layer approach and a dual-layer… More >

Syed Atir Raza^1,2, Muhammad Shoaib Farooq¹, Uzma Farooq³, Hanen Karamti ⁴, Tahir Khurshaid^5,*, Imran Ashraf^6,*

CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 3149-3173, 2025, DOI:10.32604/cmc.2025.063255 - 03 July 2025

Abstract Urdu, a prominent subcontinental language, serves as a versatile means of communication. However, its handwritten expressions present challenges for optical character recognition (OCR). While various OCR techniques have been proposed, most of them focus on recognizing printed Urdu characters and digits. To the best of our knowledge, very little research has focused solely on Urdu pure handwriting recognition, and the results of such proposed methods are often inadequate. In this study, we introduce a novel approach to recognizing Urdu pure handwritten digits and characters using Convolutional Neural Networks (CNN). Our proposed method utilizes convolutional layers… More >

Noor Ayesha¹, Muhammad Mujahid², Abeer Rashad Mirdad², Faten S. Alamri^3,*, Amjad R. Khan²

CMC-Computers, Materials & Continua, Vol.84, No.1, pp. 1883-1899, 2025, DOI:10.32604/cmc.2025.063560 - 09 June 2025

Abstract Large language models (LLMs) and natural language processing (NLP) have significant promise to improve efficiency and refine healthcare decision-making and clinical results. Numerous domains, including healthcare, are rapidly adopting LLMs for the classification of biomedical textual data in medical research. The LLM can derive insights from intricate, extensive, unstructured training data. Variants need to be accurately identified and classified to advance genetic research, provide individualized treatment, and assist physicians in making better choices. However, the sophisticated and perplexing language of medical reports is often beyond the capabilities of the devices we now utilize. Such an… More >

Sangmin Kim¹, Byeongcheon Lee¹, Muazzam Maqsood², Jihoon Moon^3,*, Seungmin Rho^4,*

CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.2, pp. 2079-2108, 2025, DOI:10.32604/cmes.2025.061653 - 30 May 2025

Abstract The increased accessibility of social networking services (SNSs) has facilitated communication and information sharing among users. However, it has also heightened concerns about digital safety, particularly for children and adolescents who are increasingly exposed to online grooming crimes. Early and accurate identification of grooming conversations is crucial in preventing long-term harm to victims. However, research on grooming detection in South Korea remains limited, as existing models trained primarily on English text and fail to reflect the unique linguistic features of SNS conversations, leading to inaccurate classifications. To address these issues, this study proposes a novel… More >