Open Access

ARTICLE

Thalassemia Screening by Sentiment Analysis on Social Media Platform Twitter

Wadhah Mohammed M. Aqlan¹, Ghassan Ahmed Ali^2,*, Khairan Rajab², Adel Rajab², Asadullah Shaikh², Fekry Olayah², Shehab Abdulhabib Saeed Alzaeemi^3,*, Kim Gaik Tay³, Mohd Adib Omar¹, Ernest Mangantig⁴

1 School of Computer Sciences, Universiti Sains Malaysia, USM, 11800, Penang, Malaysia
2 College of Computer Science and Information Systems, Najran University, Najran, 61441, Saudi Arabia
3 Faculty of Electrical and Electronic Engineering, Universiti Tun Hussein Onn Malaysia, 86400, Johor, Malaysia
4 IPPT, Universiti Sains Malaysia, USM, 11800, Penang, Malaysia

* Corresponding Authors: Ghassan Ahmed Ali. Email: ; Shehab Abdulhabib Saeed Alzaeemi. Email:

Computers, Materials & Continua 2023, 76(1), 665-686. https://doi.org/10.32604/cmc.2023.039228

Received 16 January 2023; Accepted 12 April 2023; Issue published 08 June 2023

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.

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Keywords

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