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  • Open Access

    ARTICLE

    Suggestion Mining from Opinionated Text of Big Social Media Data

    Youseef Alotaibi1,*, Muhammad Noman Malik2, Huma Hayat Khan3, Anab Batool2, Saif ul Islam4, Abdulmajeed Alsufyani5, Saleh Alghamdi6

    CMC-Computers, Materials & Continua, Vol.68, No.3, pp. 3323-3338, 2021, DOI:10.32604/cmc.2021.016727

    Abstract Social media data are rapidly increasing and constitute a source of user opinions and tips on a wide range of products and services. The increasing availability of such big data on biased reviews and blogs creates challenges for customers and businesses in reviewing all content in their decision-making process. To overcome this challenge, extracting suggestions from opinionated text is a possible solution. In this study, the characteristics of suggestions are analyzed and a suggestion mining extraction process is presented for classifying suggestive sentences from online customers’ reviews. A classification using a word-embedding approach is used via the XGBoost classifier. The… More >

  • Open Access

    REVIEW

    Analyzing Customer Reviews on Social Media via Applying Association Rule

    Nancy Awadallah Awad1,*, Amena Mahmoud2

    CMC-Computers, Materials & Continua, Vol.68, No.2, pp. 1519-1530, 2021, DOI:10.32604/cmc.2021.016974

    Abstract The rapid growth of the use of social media opens up new challenges and opportunities to analyze various aspects and patterns in communication. In-text mining, several techniques are available such as information clustering, extraction, summarization, classification. In this study, a text mining framework was presented which consists of 4 phases retrieving, processing, indexing, and mine association rule phase. It is applied by using the association rule mining technique to check the associated term with the Huawei P30 Pro phone. Customer reviews are extracted from many websites and Facebook groups, such as re-view.cnet.com, CNET. Facebook and amazon.com technology, where customers from… More >

  • Open Access

    ARTICLE

    Automatic Surveillance of Pandemics Using Big Data and Text Mining

    Abdullah Alharbi1,*, Wael Alosaimi1, M. Irfan Uddin2

    CMC-Computers, Materials & Continua, Vol.68, No.1, pp. 303-317, 2021, DOI:10.32604/cmc.2021.016230

    Abstract COVID-19 disease is spreading exponentially due to the rapid transmission of the virus between humans. Different countries have tried different solutions to control the spread of the disease, including lockdowns of countries or cities, quarantines, isolation, sanitization, and masks. Patients with symptoms of COVID-19 are tested using medical testing kits; these tests must be conducted by healthcare professionals. However, the testing process is expensive and time-consuming. There is no surveillance system that can be used as surveillance framework to identify regions of infected individuals and determine the rate of spread so that precautions can be taken. This paper introduces a… More >

  • Open Access

    ARTICLE

    Sentiment Analysis for Arabic Social Media News Polarity

    Adnan A. Hnaif1,*, Emran Kanan2, Tarek Kanan1

    Intelligent Automation & Soft Computing, Vol.28, No.1, pp. 107-119, 2021, DOI:10.32604/iasc.2021.015939

    Abstract In recent years, the use of social media has rapidly increased and developed significant influence on its users. In the study of the behavior, reactions, approval, and interactions of social media users, detecting the polarity (positive, negative, neutral) of news posts is of considerable importance. This proposed research aims to collect data from Arabic social media pages, with the posts comprising the main unit in the dataset, and to build a corpus of manually-processed data for training and testing. Applying Natural Language Processing to the data is crucial for the computer to understand and easily manipulate the data. Therefore, Stop-Word… More >

  • Open Access

    ARTICLE

    Analyzing COVID-2019 Impact on Mental Health Through Social Media Forum

    Huma1, Muhammad Khalid Sohail2, Nadeem Akhtar3, Dost Muhammad3, Humaira Afzal4, Muhammad Rafiq Mufti5, Shahid Hussain6,*, Mansoor Ahmed1

    CMC-Computers, Materials & Continua, Vol.67, No.3, pp. 3737-3748, 2021, DOI:10.32604/cmc.2021.014398

    Abstract This study aims to identify the potential association of mental health and social media forum during the outbreak of COVID-19 pandemic. COVID-19 brings a lot of challenges to government globally. Among the different strategies the most extensively adopted ones were lockdown, social distancing, and isolation among others. Most people with no mental illness history have been found with high risk of distress and psychological discomfort due to anxiety of being infected with the virus. Panic among people due to COVID-19 spread faster than the disease itself. The misinformation and excessive usage of social media in this pandemic era have adversely… More >

  • Open Access

    ARTICLE

    Social Media and Stock Market Prediction: A Big Data Approach

    Mazhar Javed Awan1,2,*, Mohd Shafry Mohd Rahim2, Haitham Nobanee3,4,5, Ashna Munawar2, Awais Yasin6, Azlan Mohd Zain 7

    CMC-Computers, Materials & Continua, Vol.67, No.2, pp. 2569-2583, 2021, DOI:10.32604/cmc.2021.014253

    Abstract Big data is the collection of large datasets from traditional and digital sources to identify trends and patterns. The quantity and variety of computer data are growing exponentially for many reasons. For example, retailers are building vast databases of customer sales activity. Organizations are working on logistics financial services, and public social media are sharing a vast quantity of sentiments related to sales price and products. Challenges of big data include volume and variety in both structured and unstructured data. In this paper, we implemented several machine learning models through Spark MLlib using PySpark, which is scalable, fast, easily integrated… More >

  • Open Access

    ARTICLE

    Detecting Information on the Spread of Dengue on Twitter Using Artificial Neural Networks

    Samina Amin1,*, M. Irfan Uddin1, M. Ali Zeb1, Ala Abdulsalam Alarood2, Marwan Mahmoud3, Monagi H. Alkinani4

    CMC-Computers, Materials & Continua, Vol.67, No.1, pp. 1317-1332, 2021, DOI:10.32604/cmc.2021.014733

    Abstract Social media platforms have lately emerged as a promising tool for predicting the outbreak of epidemics by analyzing information on them with the help of machine learning techniques. Many analytical and statistical models are available to infer a variety of user sentiments in posts on social media. The amount of data generated by social media platforms, such as Twitter, that can be used to track diseases is increasing rapidly. This paper proposes a method for the classification of tweets related to the outbreak of dengue using machine learning algorithms. An artificial neural network (ANN)-based method is developed using Global Vector… More >

  • Open Access

    ARTICLE

    Experimental Evaluation of Clickbait Detection Using Machine Learning Models

    Iftikhar Ahmad1,*, Mohammed A. Alqarni2, Abdulwahab Ali Almazroi3, Abdullah Tariq1

    Intelligent Automation & Soft Computing, Vol.26, No.6, pp. 1335-1344, 2020, DOI:10.32604/iasc.2020.013861

    Abstract The exponential growth of social media has been instrumental in directing the news outlets to rely on the stated platform for the dissemination of news stories. While social media has helped in the fast propagation of breaking news, it also has allowed many bad actors to exploit this medium for political and monetary purposes. With such an intention, tempting headlines, which are not aligned with the content, are being used to lure users to visit the websites that often post dodgy and unreliable information. This phenomenon is commonly known as clickbait. A number of machine learning techniques have been developed… More >

  • Open Access

    ARTICLE

    Autonomous Eyewitness Identification by Employing Linguistic Rules for Disaster Events

    Sajjad Haider*, Muhammad Tanvir Afzal

    CMC-Computers, Materials & Continua, Vol.66, No.1, pp. 481-498, 2021, DOI:10.32604/cmc.2020.012057

    Abstract Social networking platforms provide a vital source for disseminating information across the globe, particularly in case of disaster. These platforms are great mean to find out the real account of the disaster. Twitter is an example of such platform, which has been extensively utilized by scientific community due to its unidirectional model. It is considered a challenging task to identify eyewitness tweets about the incident from the millions of tweets shared by twitter users. Research community has proposed diverse sets of techniques to identify eyewitness account. A recent state-of-the-art approach has proposed a comprehensive set of features to identify eyewitness… More >

  • Open Access

    ARTICLE

    Finding Temporal Influential Users in Social Media Using Association Rule Learning

    Babar Shazad1, Hikmat Ullah khan2, Zahoor-ur-Rehman1, Muhammad Farooq2, Ahsan Mahmood1, Irfan Mehmood3,*, Seungmin Rho3, Yunyoung Nam4,*

    Intelligent Automation & Soft Computing, Vol.26, No.1, pp. 87-98, 2020, DOI:10.31209/2019.100000130

    Abstract The social media has become an integral part of our daily life. The social web users interact and thus influence each other influence in many aspects. Blogging is one of the most important features of the social web. The bloggers share their views, opinions and ideas in the form of blog posts. The influential bloggers are the leading bloggers who influence the other bloggers in their online communities. The relevant literature presents several studies related to identification of top influential bloggers in last decade. The research domain of finding the top influential bloggers mainly focuses on feature centric models. This… More >

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