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  • Open Access

    ARTICLE

    Multiclass Classification for Cyber Threats Detection on Twitter

    Adnan Hussein1, Abdulwahab Ali Almazroi2,*

    CMC-Computers, Materials & Continua, Vol.77, No.3, pp. 3853-3866, 2023, DOI:10.32604/cmc.2023.040856

    Abstract The advances in technology increase the number of internet systems usage. As a result, cybersecurity issues have become more common. Cyber threats are one of the main problems in the area of cybersecurity. However, detecting cybersecurity threats is not a trivial task and thus is the center of focus for many researchers due to its importance. This study aims to analyze Twitter data to detect cyber threats using a multiclass classification approach. The data is passed through different tasks to prepare it for the analysis. Term Frequency and Inverse Document Frequency (TFIDF) features are extracted to vectorize the cleaned data… More >

  • Open Access

    ARTICLE

    A Deep Learning Based Sentiment Analytic Model for the Prediction of Traffic Accidents

    Nadeem Malik1,*, Saud Altaf1, Muhammad Usman Tariq2, Ashir Ahmed3, Muhammad Babar4

    CMC-Computers, Materials & Continua, Vol.77, No.2, pp. 1599-1615, 2023, DOI:10.32604/cmc.2023.040455

    Abstract The severity of traffic accidents is a serious global concern, particularly in developing nations. Knowing the main causes and contributing circumstances may reduce the severity of traffic accidents. There exist many machine learning models and decision support systems to predict road accidents by using datasets from different social media forums such as Twitter, blogs and Facebook. Although such approaches are popular, there exists an issue of data management and low prediction accuracy. This article presented a deep learning-based sentiment analytic model known as Extra-large Network Bi-directional long short term memory (XLNet-Bi-LSTM) to predict traffic collisions based on data collected from… More >

  • Open Access

    ARTICLE

    Deer Hunting Optimization with Deep Learning Enabled Emotion Classification on English Twitter Data

    Abdelwahed Motwakel1,*, Hala J. Alshahrani2, Jaber S. Alzahrani3, Ayman Yafoz4, Heba Mohsen5, Ishfaq Yaseen1, Amgad Atta Abdelmageed1, Mohamed I. Eldesouki6

    Computer Systems Science and Engineering, Vol.47, No.3, pp. 2741-2757, 2023, DOI:10.32604/csse.2023.034721

    Abstract Currently, individuals use online social media, namely Facebook or Twitter, for sharing their thoughts and emotions. Detection of emotions on social networking sites’ finds useful in several applications in social welfare, commerce, public health, and so on. Emotion is expressed in several means, like facial and speech expressions, gestures, and written text. Emotion recognition in a text document is a content-based classification problem that includes notions from deep learning (DL) and natural language processing (NLP) domains. This article proposes a Deer Hunting Optimization with Deep Belief Network Enabled Emotion Classification (DHODBN-EC) on English Twitter Data in this study. The presented… More >

  • Open Access

    ARTICLE

    Improving Sentiment Analysis in Election-Based Conversations on Twitter with ElecBERT Language Model

    Asif Khan1, Huaping Zhang1,*, Nada Boudjellal2, Arshad Ahmad3, Maqbool Khan3

    CMC-Computers, Materials & Continua, Vol.76, No.3, pp. 3345-3361, 2023, DOI:10.32604/cmc.2023.041520

    Abstract Sentiment analysis plays a vital role in understanding public opinions and sentiments toward various topics. In recent years, the rise of social media platforms (SMPs) has provided a rich source of data for analyzing public opinions, particularly in the context of election-related conversations. Nevertheless, sentiment analysis of election-related tweets presents unique challenges due to the complex language used, including figurative expressions, sarcasm, and the spread of misinformation. To address these challenges, this paper proposes Election-focused Bidirectional Encoder Representations from Transformers (ElecBERT), a new model for sentiment analysis in the context of election-related tweets. Election-related tweets pose unique challenges for sentiment… More >

  • Open Access

    ARTICLE

    Deep Learning Based Cyber Event Detection from Open-Source Re-Emerging Social Data

    Farah Mohammad1,*, Saad Al-Ahmadi2, Jalal Al-Muhtadi1,2

    CMC-Computers, Materials & Continua, Vol.76, No.2, pp. 1423-1438, 2023, DOI:10.32604/cmc.2023.035741

    Abstract Social media forums have emerged as the most popular form of communication in the modern technology era, allowing people to discuss and express their opinions. This increases the amount of material being shared on social media sites. There is a wealth of information about the threat that may be found in such open data sources. The security of already-deployed software and systems relies heavily on the timely detection of newly-emerging threats to their safety that can be gleaned from such information. Despite the fact that several models for detecting cybersecurity events have been presented, it remains challenging to extract security… More >

  • Open Access

    ARTICLE

    Analyzing Arabic Twitter-Based Patient Experience Sentiments Using Multi-Dialect Arabic Bidirectional Encoder Representations from Transformers

    Sarab AlMuhaideb*, Yasmeen AlNegheimish, Taif AlOmar, Reem AlSabti, Maha AlKathery, Ghala AlOlyyan

    CMC-Computers, Materials & Continua, Vol.76, No.1, pp. 195-220, 2023, DOI:10.32604/cmc.2023.038368

    Abstract Healthcare organizations rely on patients’ feedback and experiences to evaluate their performance and services, thereby allowing such organizations to improve inadequate services and address any shortcomings. According to the literature, social networks and particularly Twitter are effective platforms for gathering public opinions. Moreover, recent studies have used natural language processing to measure sentiments in text segments collected from Twitter to capture public opinions about various sectors, including healthcare. The present study aimed to analyze Arabic Twitter-based patient experience sentiments and to introduce an Arabic patient experience corpus. The authors collected 12,400 tweets from Arabic patients discussing patient experiences related to… More >

  • Open Access

    ARTICLE

    Thalassemia Screening by Sentiment Analysis on Social Media Platform Twitter

    Wadhah Mohammed M. Aqlan1, Ghassan Ahmed Ali2,*, Khairan Rajab2, Adel Rajab2, Asadullah Shaikh2, Fekry Olayah2, Shehab Abdulhabib Saeed Alzaeemi3,*, Kim Gaik Tay3, Mohd Adib Omar1, Ernest Mangantig4

    CMC-Computers, Materials & Continua, Vol.76, No.1, pp. 665-686, 2023, DOI:10.32604/cmc.2023.039228

    Abstract Thalassemia syndrome is a genetic blood disorder induced by the reduction of normal hemoglobin production, resulting in a drop in the size of red blood cells. In severe forms, it can lead to death. This genetic disorder has posed a major burden on public health wherein patients with severe thalassemia need periodic therapy of iron chelation and blood transfusion for survival. Therefore, controlling thalassemia is extremely important and is made by promoting screening to the general population, particularly among thalassemia carriers. Today Twitter is one of the most influential social media platforms for sharing opinions and discussing different topics like… More >

  • Open Access

    ARTICLE

    Sine Cosine Optimization with Deep Learning-Based Applied Linguistics for Sentiment Analysis on COVID-19 Tweets

    Abdelwahed Motwakel1,*, Hala J. Alshahrani2, Abdulkhaleq Q. A. Hassan3, Khaled Tarmissi4, Amal S. Mehanna5, Ishfaq Yaseen1, Amgad Atta Abdelmageed1, Mohammad Mahzari6

    CMC-Computers, Materials & Continua, Vol.75, No.3, pp. 4767-4783, 2023, DOI:10.32604/cmc.2023.034840

    Abstract Applied linguistics is an interdisciplinary domain which identifies, investigates, and offers solutions to language-related real-life problems. The new coronavirus disease, otherwise known as Coronavirus disease (COVID-19), has severely affected the everyday life of people all over the world. Specifically, since there is insufficient access to vaccines and no straight or reliable treatment for coronavirus infection, the country has initiated the appropriate preventive measures (like lockdown, physical separation, and masking) for combating this extremely transmittable disease. So, individuals spent more time on online social media platforms (i.e., Twitter, Facebook, Instagram, LinkedIn, and Reddit) and expressed their thoughts and feelings about coronavirus… More >

  • Open Access

    ARTICLE

    Parameter Tuned Machine Learning Based Emotion Recognition on Arabic Twitter Data

    Ibrahim M. Alwayle1, Badriyya B. Al-onazi2, Jaber S. Alzahrani3, Khaled M. Alalayah1, Khadija M. Alaidarous1, Ibrahim Abdulrab Ahmed4, Mahmoud Othman5, Abdelwahed Motwakel6,*

    Computer Systems Science and Engineering, Vol.46, No.3, pp. 3423-3438, 2023, DOI:10.32604/csse.2023.033834

    Abstract Arabic is one of the most spoken languages across the globe. However, there are fewer studies concerning Sentiment Analysis (SA) in Arabic. In recent years, the detected sentiments and emotions expressed in tweets have received significant interest. The substantial role played by the Arab region in international politics and the global economy has urged the need to examine the sentiments and emotions in the Arabic language. Two common models are available: Machine Learning and lexicon-based approaches to address emotion classification problems. With this motivation, the current research article develops a Teaching and Learning Optimization with Machine Learning Based Emotion Recognition… More >

  • Open Access

    ARTICLE

    Gender Identification Using Marginalised Stacked Denoising Autoencoders on Twitter Data

    Badriyya B. Al-onazi1, Mohamed K. Nour2, Hassan Alshamrani3, Mesfer Al Duhayyim4,*, Heba Mohsen5, Amgad Atta Abdelmageed6, Gouse Pasha Mohammed6, Abu Sarwar Zamani6

    Intelligent Automation & Soft Computing, Vol.36, No.3, pp. 2529-2544, 2023, DOI:10.32604/iasc.2023.034623

    Abstract Gender analysis of Twitter could reveal significant socio-cultural differences between female and male users. Efforts had been made to analyze and automatically infer gender formerly for more commonly spoken languages’ content, but, as we now know that limited work is being undertaken for Arabic. Most of the research works are done mainly for English and least amount of effort for non-English language. The study for Arabic demographic inference like gender is relatively uncommon for social networking users, especially for Twitter. Therefore, this study aims to design an optimal marginalized stacked denoising autoencoder for gender identification on Arabic Twitter (OMSDAE-GIAT) model.… More >

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