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  • Open Access

    ARTICLE

    Optimal Quad Channel Long Short-Term Memory Based Fake News Classification on English Corpus

    Manar Ahmed Hamza1,*, Hala J. Alshahrani2, Khaled Tarmissi3, Ayman Yafoz4, Amal S. Mehanna5, Ishfaq Yaseen1, Amgad Atta Abdelmageed1, Mohamed I. Eldesouki6

    Computer Systems Science and Engineering, Vol.46, No.3, pp. 3303-3319, 2023, DOI:10.32604/csse.2023.034823

    Abstract The term ‘corpus’ refers to a huge volume of structured datasets containing machine-readable texts. Such texts are generated in a natural communicative setting. The explosion of social media permitted individuals to spread data with minimal examination and filters freely. Due to this, the old problem of fake news has resurfaced. It has become an important concern due to its negative impact on the community. To manage the spread of fake news, automatic recognition approaches have been investigated earlier using Artificial Intelligence (AI) and Machine Learning (ML) techniques. To perform the medicinal text classification tasks, the ML approaches were applied, and… More >

  • Open Access

    ARTICLE

    Computational Linguistics with Optimal Deep Belief Network Based Irony Detection in Social Media

    Manar Ahmed Hamza1,*, Hala J. Alshahrani2, Abdulkhaleq Q. A. Hassan3, Abdulbaset Gaddah4, Nasser Allheeib5, Suleiman Ali Alsaif6, Badriyya B. Al-onazi7, Heba Mohsen8

    CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 4137-4154, 2023, DOI:10.32604/cmc.2023.035237

    Abstract Computational linguistics refers to an interdisciplinary field associated with the computational modelling of natural language and studying appropriate computational methods for linguistic questions. The number of social media users has been increasing over the last few years, which have allured researchers’ interest in scrutinizing the new kind of creative language utilized on the Internet to explore communication and human opinions in a better way. Irony and sarcasm detection is a complex task in Natural Language Processing (NLP). Irony detection has inferences in advertising, sentiment analysis (SA), and opinion mining. For the last few years, irony-aware SA has gained significant computational… More >

  • Open Access

    ARTICLE

    Bayesian Deep Learning Enabled Sentiment Analysis on Web Intelligence Applications

    Abeer D. Algarni*

    CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 3399-3412, 2023, DOI:10.32604/cmc.2023.026687

    Abstract In recent times, web intelligence (WI) has become a hot research topic, which utilizes Artificial Intelligence (AI) and advanced information technologies on the Web and Internet. The users post reviews on social media and are employed for sentiment analysis (SA), which acts as feedback to business people and government. Proper SA on the reviews helps to enhance the quality of the services and products, however, web intelligence techniques are needed to raise the company profit and user fulfillment. With this motivation, this article introduces a new modified pigeon inspired optimization based feature selection (MPIO-FS) with Bayesian deep learning (BDL), named… More >

  • Open Access

    ARTICLE

    Classifying Misinformation of User Credibility in Social Media Using Supervised Learning

    Muhammad Asfand-e-Yar1,*, Qadeer Hashir1,*, Syed Hassan Tanvir1, Wajeeha Khalil2

    CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 2921-2938, 2023, DOI:10.32604/cmc.2023.034741

    Abstract The growth of the internet and technology has had a significant effect on social interactions. False information has become an important research topic due to the massive amount of misinformed content on social networks. It is very easy for any user to spread misinformation through the media. Therefore, misinformation is a problem for professionals, organizers, and societies. Hence, it is essential to observe the credibility and validity of the News articles being shared on social media. The core challenge is to distinguish the difference between accurate and false information. Recent studies focus on News article content, such as News titles… More >

  • Open Access

    ARTICLE

    Analysis of Social Media Impact on Stock Price Movements Using Machine Learning Anomaly Detection

    Richard Cruz1, Johnson Kinyua1,*, Charles Mutigwe2

    Intelligent Automation & Soft Computing, Vol.36, No.3, pp. 3405-3423, 2023, DOI:10.32604/iasc.2023.035906

    Abstract The massive increase in the volume of data generated by individuals on social media microblog platforms such as Twitter and Reddit every day offers researchers unique opportunities to analyze financial markets from new perspectives. The meme stock mania of 2021 brought together stock traders and investors that were also active on social media. This mania was in good part driven by retail investors’ discussions on investment strategies that occurred on social media platforms such as Reddit during the COVID-19 lockdowns. The stock trades by these retail investors were then executed using services like Robinhood. In this paper, machine learning models… 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 >

  • Open Access

    ARTICLE

    Political Optimizer with Probabilistic Neural Network-Based Arabic Comparative Opinion Mining

    Najm Alotaibi1, Badriyya B. Al-onazi2, Mohamed K. Nour3, Abdullah Mohamed4, Abdelwahed Motwakel5,*, Gouse Pasha Mohammed5, Ishfaq Yaseen5, Mohammed Rizwanullah5

    Intelligent Automation & Soft Computing, Vol.36, No.3, pp. 3121-3137, 2023, DOI:10.32604/iasc.2023.033915

    Abstract Opinion Mining (OM) studies in Arabic are limited though it is one of the most extensively-spoken languages worldwide. Though the interest in OM studies in the Arabic language is growing among researchers, it needs a vast number of investigations due to the unique morphological principles of the language. Arabic OM studies experience multiple challenges owing to the poor existence of language sources and Arabic-specific linguistic features. The comparative OM studies in the English language are wide and novel. But, comparative OM studies in the Arabic language are yet to be established and are still in a nascent stage. The unique… More >

  • Open Access

    ARTICLE

    A Deep Learning Model to Analyse Social-Cyber Psychological Problems in Youth

    Ali Alqazzaz1, Mohammad Tabrez Quasim1,*, Mohammed Mujib Alshahrani1, Ibrahim Alrashdi2, Mohammad Ayoub Khan1

    Computer Systems Science and Engineering, Vol.46, No.1, pp. 551-562, 2023, DOI:10.32604/csse.2023.031048

    Abstract Facebook, Twitter, Instagram, and other social media have emerged as excellent platforms for interacting with friends and expressing thoughts, posts, comments, images, and videos that express moods, sentiments, and feelings. With this, it has become possible to examine user thoughts and feelings in social network data to better understand their perspectives and attitudes. However, the analysis of depression based on social media has gained widespread acceptance worldwide, other verticals still have yet to be discovered. The depression analysis uses Twitter data from a publicly available web source in this work. To assess the accuracy of depression detection, long-short-term memory (LSTM)… More >

  • Open Access

    ARTICLE

    EliteVec: Feature Fusion for Depression Diagnosis Using Optimized Long Short-Term Memory Network

    S. Kavi Priya*, K. Pon Karthika

    Intelligent Automation & Soft Computing, Vol.36, No.2, pp. 1745-1766, 2023, DOI:10.32604/iasc.2023.032160

    Abstract Globally, depression is perceived as the most recurrent and risky disorder among young people and adults under the age of 60. Depression has a strong influence on the usage of words which can be observed in the form of written texts or stories posted on social media. With the help of Natural Language Processing(NLP) and Machine Learning (ML) techniques, the depressive signs expressed by people can be identified at the earliest stage from their Social Media posts. The proposed work aims to introduce an efficacious depression detection model unifying an exemplary feature extraction scheme and a hybrid Long Short-Term Memory… More >

  • Open Access

    ARTICLE

    Multi-Tier Sentiment Analysis of Social Media Text Using Supervised Machine Learning

    Hameedur Rahman1, Junaid Tariq2,*, M. Ali Masood1, Ahmad F. Subahi3, Osamah Ibrahim Khalaf4, Youseef Alotaibi5

    CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 5527-5543, 2023, DOI:10.32604/cmc.2023.033190

    Abstract Sentiment Analysis (SA) is often referred to as opinion mining. It is defined as the extraction, identification, or characterization of the sentiment from text. Generally, the sentiment of a textual document is classified into binary classes i.e., positive and negative. However, fine-grained classification provides a better insight into the sentiments. The downside is that fine-grained classification is more challenging as compared to binary. On the contrary, performance deteriorates significantly in the case of multi-class classification. In this study, pre-processing techniques and machine learning models for the multi-class classification of sentiments were explored. To augment the performance, a multi-layer classification model… More >

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