Detecting Infectious Disease Based on Social Media Data Using BERT Model
subject: BERT; Influenza; Seasonal Infectious Disease; Sentiment Analysis
description: Seasonal diseases are those diseases that are widespread during a particular time of the year including monsoons, winter etc. In the absence of preventative measures, the human race remains vulnerable to the hazardous effects of seasonal diseases following regular patterns of increased inci- dence and transmission which remains a global concern. Dengue, Influenza, etc. are such types of diseases where every year many people get affected globally. The primary focus of this research paper is to understand the opinion of people regarding the seasonal diseases. The research paper covers sentiment analysis on textual data from social media where people have vocalized their sentiments or thinking regarding seasonal diseases and seasonal infectious diseases. Influenza, Dengue, Malaria, Japanese Encephalitis, and Chikungunya are the seasonal diseases that have been covered in this research paper. To achieve this, the language model Bidirectional Encoder Representations from Transformers (BERT) was used to verify the sentiments about the seasonal diseases. The result of the investigation hold the potential to significantly enhance our comprehension of societal sentiments, discerning between states of tranquility and concern among individuals. The outcome of the study will help healthcare department to plan the necessary actions. 2024 IEEE.
creator: Barman R.; Rajamohan K.
source: Proceedings of InC4 2024 - 2024 IEEE International Conference on Contemporary Computing and Communications
publisher: Institute of Electrical and Electronics Engineers Inc.
date: 2024-01-01
rights: Restricted Access
relation: ISBN: 979-835038365-2
format: Online
language: English
type: Conference paper
coverage: Barman R., CHRIST University, Department of Computer Science, Bangalore, India; Rajamohan K., CHRIST University, Department of Computer Science, Bangalore, India