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  • Open Access


    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… More >

  • Open Access


    Drug Usage Safety from Drug Reviews with Hybrid Machine Learning Approach

    Ernesto Lee1, Furqan Rustam2, Hina Fatima Shahzad2, Patrick Bernard Washington3, Abid Ishaq3, Imran Ashraf4,*

    Computer Systems Science and Engineering, Vol.45, No.3, pp. 3053-3077, 2023, DOI:10.32604/csse.2023.029059

    Abstract With the increasing usage of drugs to remedy different diseases, drug safety has become crucial over the past few years. Often medicine from several companies is offered for a single disease that involves the same/similar substances with slightly different formulae. Such diversification is both helpful and dangerous as such medicine proves to be more effective or shows side effects to different patients. Despite clinical trials, side effects are reported when the medicine is used by the mass public, of which several such experiences are shared on social media platforms. A system capable of analyzing such… More >

  • Open Access


    Automatic Annotation Performance of TextBlob and VADER on Covid Vaccination Dataset

    Badriya Murdhi Alenzi, Muhammad Badruddin Khan, Mozaherul Hoque Abul Hasanat, Abdul Khader Jilani Saudagar*, Mohammed AlKhathami, Abdullah AlTameem

    Intelligent Automation & Soft Computing, Vol.34, No.2, pp. 1311-1331, 2022, DOI:10.32604/iasc.2022.025861

    Abstract With the recent boom in the corpus size of sentiment analysis tasks, automatic annotation is poised to be a necessary alternative to manual annotation for generating ground truth dataset labels. This article aims to investigate and validate the performance of two widely used lexicon-based automatic annotation approaches, TextBlob and Valence Aware Dictionary and Sentiment Reasoner (VADER), by comparing them with manual annotation. The dataset of 5402 Arabic tweets was annotated manually, containing 3124 positive tweets, 1463 negative tweets, and 815 neutral tweets. The tweets were translated into English so that TextBlob and VADER could be More >

  • Open Access


    MELex: The Construction of Malay-English Sentiment Lexicon

    Nurul Husna Mahadzir1, Mohd Faizal Omar2, Mohd Nasrun Mohd Nawi3,*, Anas A. Salameh4, Kasmaruddin Che Hussin5, Abid Sohail6

    CMC-Computers, Materials & Continua, Vol.71, No.1, pp. 1789-1805, 2022, DOI:10.32604/cmc.2022.021131

    Abstract Currently, the sentiment analysis research in the Malaysian context lacks in terms of the availability of the sentiment lexicon. Thus, this issue is addressed in this paper in order to enhance the accuracy of sentiment analysis. In this study, a new lexicon for sentiment analysis is constructed. A detailed review of existing approaches has been conducted, and a new bilingual sentiment lexicon known as MELex (Malay-English Lexicon) has been generated. Constructing MELex involves three activities: seed words selection, polarity assignment, and synonym expansions. Our approach differs from previous works in that MELex can analyze text More >

  • Open Access


    Fuzzy-Based Sentiment Analysis System for Analyzing Student Feedback and Satisfaction

    Yun Wang1, Fazli Subhan2, Shahaboddin Shamshirband3, 4, *, Muhammad Zubair Asghar5, Ikram Ullah5, Ammara Habib5

    CMC-Computers, Materials & Continua, Vol.62, No.2, pp. 631-655, 2020, DOI:10.32604/cmc.2020.07920

    Abstract The feedback collection and analysis has remained an important subject matter for long. The traditional techniques for student feedback analysis are based on questionnaire-based data collection and analysis. However, the student expresses their feedback opinions on online social media sites, which need to be analyzed. This study aims at the development of fuzzy-based sentiment analysis system for analyzing student feedback and satisfaction by assigning proper sentiment score to opinion words and polarity shifters present in the input reviews. Our technique computes the sentiment score of student feedback reviews and then applies a fuzzy-logic module to More >

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