Work

HaleLab_NITK@SMM4H’24: Binary classification of English tweets reporting children’s medical disorders

Year: 2024

Type: conference-paper

Abstract: This paper describes the work undertaken as part of the SMM4H-2024 shared task, specifically Task 5, which involves the binary classification of English tweets reporting children's medical disorders.The primary objective is to develop a system capable of automatically identifying tweets from users who report their pregnancy and mention children with specific medical conditions, such as attentiondeficit/hyperactivity disorder (ADHD), autism spectrum disorders (ASD), delayed speech, or asthma, while distinguishing them from tweets that merely reference a disorder without much context.Our approach leverages advanced natural language processing techniques and machine learning algorithms to accurately classify the tweets.The system achieved an overall F1score of 0.87, highlighting its robustness and effectiveness in addressing the classification challenge posed by this task.

Source: Social Media Mining for Health Research and Applications Workshop and Shared Tasks (SMM4H)

DOI: 10.18653/v1/2024.smm4h-1.31

Authors: Ritik Mahajan, Sowmya S.

Institution: National Institute of Technology Karnataka

Language: English

Topic: Sentiment Analysis and Opinion Mining

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

SDG: Good health and well-being

Open Access status: hybrid

OpenAlex ID: W4402683370