Abdullah R. Alshamsan¹, Shafique A. Chaudhry^1,2,*

CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 4631-4647, 2023, DOI:10.32604/cmc.2023.034039

Abstract Data privacy laws require service providers to inform their customers on how user data is gathered, used, protected, and shared. The General Data Protection Regulation (GDPR) is a legal framework that provides guidelines for collecting and processing personal information from individuals. Service providers use privacy policies to outline the ways an organization captures, retains, analyzes, and shares customers’ data with other parties. These policies are complex and written using legal jargon; therefore, users rarely read them before accepting them. There exist a number of approaches to automating the task of summarizing privacy policies and assigning risk levels. Most of the… More >

Badriyya B. Al-onazi¹, Saud S. Alotaib², Saeed Masoud Alshahrani^3,*, Najm Alotaibi⁴, Mrim M. Alnfiai⁵, Ahmed S. Salama⁶, Manar Ahmed Hamza⁷

CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 5447-5465, 2023, DOI:10.32604/cmc.2023.033564

Abstract The text classification process has been extensively investigated in various languages, especially English. Text classification models are vital in several Natural Language Processing (NLP) applications. The Arabic language has a lot of significance. For instance, it is the fourth mostly-used language on the internet and the sixth official language of the United Nations. However, there are few studies on the text classification process in Arabic. A few text classification studies have been published earlier in the Arabic language. In general, researchers face two challenges in the Arabic text classification process: low accuracy and high dimensionality of the features. In this… More >

Abdelwahed Motwakel^1,*, Badriyya B. Al-onazi², Jaber S. Alzahrani³, Radwa Marzouk⁴, Amira Sayed A. Aziz⁵, Abu Sarwar Zamani¹, Ishfaq Yaseen¹, Amgad Atta Abdelmageed¹

Computer Systems Science and Engineering, Vol.45, No.3, pp. 3097-3113, 2023, DOI:10.32604/csse.2023.033945

Abstract With a population of 440 million, Arabic language users form the rapidly growing language group on the web in terms of the number of Internet users. 11 million monthly Twitter users were active and posted nearly 27.4 million tweets every day. In order to develop a classification system for the Arabic language there comes a need of understanding the syntactic framework of the words thereby manipulating and representing the words for making their classification effective. In this view, this article introduces a Dolphin Swarm Optimization with Convolutional Deep Belief Network for Short Text Classification (DSOCDBN-STC) model on Arabic Corpus. The… More >

Muhammad Aasim Qureshi¹, Muhammad Asif², Saira Anwar³, Umar Shaukat¹, Atta-ur-Rahman⁴, Muhammad Adnan Khan^5,*, Amir Mosavi^6,7,8

CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 2589-2605, 2023, DOI:10.32604/cmc.2023.032173

Abstract With the advancements in internet facilities, people are more inclined towards the use of online services. The service providers shelve their items for e-users. These users post their feedbacks, reviews, ratings, etc. after the use of the item. The enormous increase in these reviews has raised the need for an automated system to analyze these reviews to rate these items. Sentiment Analysis (SA) is a technique that performs such decision analysis. This research targets the ranking and rating through sentiment analysis of these reviews, on different aspects. As a case study, Songs are opted to design and test the decision… More >

Samuel Kofi Akpatsa^1,*, Hang Lei¹, Xiaoyu Li¹, Victor-Hillary Kofi Setornyo Obeng¹, Ezekiel Mensah Martey¹, Prince Clement Addo², Duncan Dodzi Fiawoo³

Journal of Quantum Computing, Vol.4, No.1, pp. 1-11, 2022, DOI:10.32604/jqc.2022.026658

Abstract The ability of pre-trained BERT model to achieve outstanding performances on many Natural Language Processing (NLP) tasks has attracted the attention of researchers in recent times. However, the huge computational and memory requirements have hampered its widespread deployment on devices with limited resources. The concept of knowledge distillation has shown to produce smaller and faster distilled models with less trainable parameters and intended for resource-constrained environments. The distilled models can be fine-tuned with great performance on a wider range of tasks, such as sentiment classification. This paper evaluates the performance of DistilBERT model and other pre-canned text classifiers on a… More >

R. Sarasu^1,*, K. K. Thyagharajan², N. R. Shanker³

Intelligent Automation & Soft Computing, Vol.35, No.2, pp. 1799-1813, 2023, DOI:10.32604/iasc.2023.027429

Abstract Researchers and scientists need rapid access to text documents such as research papers, source code and dissertations. Many research documents are available on the Internet and need more time to retrieve exact documents based on keywords. An efficient classification algorithm for retrieving documents based on keyword words is required. The traditional algorithm performs less because it never considers words’ polysemy and the relationship between bag-of-words in keywords. To solve the above problem, Semantic Featured Convolution Neural Networks (SF-CNN) is proposed to obtain the key relationships among the searching keywords and build a structure for matching the words for retrieving correct… More >

Md. Anwar Hussen Wadud¹, M. F. Mridha¹, Jungpil Shin^2,*, Kamruddin Nur³, Aloke Kumar Saha⁴

Computer Systems Science and Engineering, Vol.44, No.2, pp. 1775-1791, 2023, DOI:10.32604/csse.2023.027841

Abstract Offensive messages on social media, have recently been frequently used to harass and criticize people. In recent studies, many promising algorithms have been developed to identify offensive texts. Most algorithms analyze text in a unidirectional manner, where a bidirectional method can maximize performance results and capture semantic and contextual information in sentences. In addition, there are many separate models for identifying offensive texts based on monolingual and multilingual, but there are a few models that can detect both monolingual and multilingual-based offensive texts. In this study, a detection system has been developed for both monolingual and multilingual offensive texts by… More >

Hanan A. Hosni Mahmoud¹, Alaaeldin M. Hafez², Eatedal Alabdulkreem^1,*

Intelligent Automation & Soft Computing, Vol.35, No.1, pp. 321-334, 2023, DOI:10.32604/iasc.2023.026235

Abstract Languages–independent text tokenization can aid in classification of languages with few sources. There is a global research effort to generate text classification for any language. Human text classification is a slow procedure. Consequently, the text summary generation of different languages, using machine text classification, has been considered in recent years. There is no research on the machine text classification for many languages such as Czech, Rome, Urdu. This research proposes a cross-language text tokenization model using a Transformer technique. The proposed Transformer employs an encoder that has ten layers with self-attention encoding and a feedforward sublayer. This model improves the… More >

Hanxu Wang, Xin Li^*

Journal on Big Data, Vol.4, No.1, pp. 41-60, 2022, DOI:10.32604/jbd.2022.027717

Abstract With the explosive growth of Internet text information, the task of text classification is more important. As a part of text classification, Chinese news text classification also plays an important role. In public security work, public opinion news classification is an important topic. Effective and accurate classification of public opinion news is a necessary prerequisite for relevant departments to grasp the situation of public opinion and control the trend of public opinion in time. This paper introduces a combined-convolutional neural network text classification model based on word2vec and improved TF-IDF: firstly, the word vector is trained through word2vec model, then… More >

Dheeraj Kumar Dixit^*, Amit Bhagat, Dharmendra Dangi

CMC-Computers, Materials & Continua, Vol.71, No.3, pp. 5733-5750, 2022, DOI:10.32604/cmc.2022.023628

Abstract In recent years, social media platforms have gained immense popularity. As a result, there has been a tremendous increase in content on social media platforms. This content can be related to an individual's sentiments, thoughts, stories, advertisements, and news, among many other content types. With the recent increase in online content, the importance of identifying fake and real news has increased. Although, there is a lot of work present to detect fake news, a study on Fuzzy CRNN was not explored into this direction. In this work, a system is designed to classify fake and real news using fuzzy logic.… More >