Thalassemia Screening by Sentiment Analysis on Social Media Platform Twitter

被引:0
|
作者
Aqlan, Wadhah Mohammed M. [1 ]
Ali, Ghassan Ahmed [2 ]
Rajab, Khairan [2 ]
Rajab, Adel [2 ]
Shaikh, Asadullah [2 ]
Olayah, Fekry [2 ]
Alzaeemi, Shehab Abdulhabib Saeed [3 ]
Tay, Kim Gaik [3 ]
Omar, Mohd Adib [1 ]
Mangantig, Ernest [4 ]
机构
[1] Univ Sains Malaysia, Sch Comp Sci, USM, George Town 11800, Malaysia
[2] Najran Univ, Coll Comp Sci & Informat Syst, Najran 61441, Saudi Arabia
[3] Univ Tun Hussein Onn Malaysia, Fac Elect & Elect Engn, Johor Baharu 86400, Malaysia
[4] Univ Sains Malaysia, IPPT, USM, George Town 11800, Malaysia
来源
CMC-COMPUTERS MATERIALS & CONTINUA | 2023年 / 76卷 / 01期
关键词
Social media platform; Twitter; screening; thalassemia; lexicon VADER;
D O I
10.32604/cmc.2023.039228
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
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 people's health conditions and major public health affairs. Exploring individuals' sentiments in these tweets helps the research centers to formulate strate-gies to promote thalassemia screening to the public. An effective Lexicon-based approach has been introduced in this study by highlighting a classifier called valence aware dictionary for sentiment reasoning (VADER). In this study applied twitter intelligence tool (TWINT), Natural Language Toolkit (NLTK), and VADER constitute the three main tools. VADER represents a gold-standard sentiment lexicon, which is basically tailored to attitudes that are communicated by using social media. The contribution of this study is to introduce an effective Lexicon-based approach by highlighting a classifier called VADER to analyze the sentiment of the general population, particularly among thalassemia carriers on the social media platform Twitter. In this study, the results showed that the proposed approach achieved 0.829, 0.816, and 0.818 regarding precision, recall, together with F-score, respectively. The tweets were crawled using the search keywords, "thalassemia screening," thalassemia test, "and thalassemia diagnosis". Finally, results showed that India and Pakistan ranked the highest in mentions in tweets by the public's conversations on thalassemia screening with 181 and 164 tweets, respectively.
引用
收藏
页码:665 / 686
页数:22
相关论文
共 50 条
  • [41] Purchase Intention and Sentiment Analysis on Twitter Related to Social Commerce
    Virgananda, Muhammad Alviazra
    Budi, Indra
    Suryono, Ryan Randy
    INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS, 2023, 14 (07) : 543 - 550
  • [42] Sentiment Analysis of Serious Suicide References in Twitter Social Network
    Korani, Wael
    Mouhoub, Malek
    ICAART: PROCEEDINGS OF THE 12TH INTERNATIONAL CONFERENCE ON AGENTS AND ARTIFICIAL INTELLIGENCE, VOL 2, 2020, : 339 - 346
  • [43] Real-Time Live Insult Analysis on Twitter-X Social Media Platform
    Sahin, Fatih
    FORTHCOMING NETWORKS AND SUSTAINABILITY IN THE AIOT ERA, VOL 2, FONES-AIOT 2024, 2024, 1036 : 328 - 338
  • [44] Sentiment Analysis of Breast Cancer Screening in the United States using Twitter
    Wong, Kai O.
    Davis, Faith G.
    Zaiane, Osmar R.
    Yasui, Yutaka
    KDIR: PROCEEDINGS OF THE 8TH INTERNATIONAL JOINT CONFERENCE ON KNOWLEDGE DISCOVERY, KNOWLEDGE ENGINEERING AND KNOWLEDGE MANAGEMENT - VOL. 1, 2016, : 265 - 274
  • [45] A Sentiment Analysis Web Platform for Multiple Social Media Types and Language-Specific Customizations
    Giannakis, Stavros
    Valavani, Christina
    Alexandris, Christina
    HUMAN-COMPUTER INTERACTION: THEORY, METHODS AND TOOLS, HCII 2021, PT I, 2021, 12762 : 318 - 328
  • [46] A SOCIAL MEDIA ANALYSIS OF #SUNSAFESELFIE ON INSTAGRAM AND TWITTER
    Nguyen, Jenn
    Holman, Dawn M.
    Patel, Ravi
    Pagoto, Sherry
    ANNALS OF BEHAVIORAL MEDICINE, 2019, 53 : S303 - S303
  • [47] HOW ARE TWITTER ACTIVITIES RELATED TO TOP CRYPTOCURRENCIES' PERFORMANCE? EVIDENCE FROM SOCIAL MEDIA NETWORK AND SENTIMENT ANALYSIS
    Park, Han Woo
    Lee, Youngjoo
    DRUSTVENA ISTRAZIVANJA, 2019, 28 (03): : 435 - 460
  • [48] Sentiment Analysis on Social Media (Twitter) about Vaccine-19 Using Support Vector Machine Algorithm
    Sulistyono, Agus
    Mulyani, Sri
    Yossy, Emny Harna
    Khalida, Rakhmi
    2021 4TH INTERNATIONAL SEMINAR ON RESEARCH OF INFORMATION TECHNOLOGY AND INTELLIGENT SYSTEMS (ISRITI 2021), 2020,
  • [49] Consumer perceptions of AI chatbots on Twitter (X) and Reddit: an analysis of social media sentiment and interactive marketing strategies
    Graham, Christian
    Stough, Rusty
    JOURNAL OF RESEARCH IN INTERACTIVE MARKETING, 2025,
  • [50] Sentiment Analysis in Social Media: A Comprehensive Bibliometric Analysis
    Tasente, Tanase
    Caratas, Maria Alina
    ADCOMUNICA-REVISTA CIENTIFICA DE ESTRATEGIAS TENDENCIAS E INNOVACION EN COMMUNICACION, 2024, (28): : 243 - 270