Thalassemia Screening by Sentiment Analysis on Social Media Platform Twitter

• Main Authors: M. Aqlan, Wadhah Mohammed, Ahmed Ali, Ghassan, Rajab, Khairan, Rajab, Adel, Shaikh, Asadullah, Olayah, Fekry, Saeed Alzaeemi, Shehab Abdulhabib, Tay, Kim Gaik, Omar, Mohd Adib, Mangantig, Ernest
• Format: Article
• Language: English
• Published: Tech Science Press 2023
• Subjects: T Technology (General)
• Online Access: http://eprints.uthm.edu.my/11656/1/J16176_322d6357c55fae674c2ebf71860cbe4d.pdf; http://eprints.uthm.edu.my/11656/; http://dx.doi.org/10.32604/cmc.2023.039228

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.