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
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