Thalassemia Screening by Sentiment Analysis on Social Media Platform Twitter

M. Aqlan, Wadhah Mohammed and Ghassan Ahmed Ali, Ghassan Ahmed Ali and Khairan Rajab, Khairan Rajab and Adel Rajab, Adel Rajab and Asadullah Shaikh, Asadullah Shaikh and Fekry Olayah, Fekry Olayah and Shehab Abdulhabib Saeed Alzaeemi, Shehab Abdulhabib Saeed Alzaeemi and Kim Gaik Tay, Kim Gaik Tay and Omar, Mohd Adib and Ernest Mangantig, Ernest Mangantig (2023) Thalassemia Screening by Sentiment Analysis on Social Media Platform Twitter. Computers, Materials & Continua, 76 (1). pp. 665-686.

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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 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 strategies to promote thalassemia screening to the public. An effective Lexiconbased 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.

Item Type:	Article
Uncontrolled Keywords:	Social media platform; Twitter; screening; thalassemia; lexicon-based; VADER
Subjects:	T Technology > T Technology (General)
Divisions:	Faculty of Electrical and Electronic Engineering > Department of Electrical Engineering
Depositing User:	Mr. Mohamad Zulkhibri Rahmad
Date Deposited:	11 Oct 2023 07:23
Last Modified:	11 Oct 2023 07:23
URI:	http://eprints.uthm.edu.my/id/eprint/10086

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