Malaria Cell Classification through Exercising Deep Learning Algorithms

Abstract

The overall spread of the Coronavirus disease 2019 (COVID-19) irresistible sickness came about with a pandemic that has compromised a large number of lives. Infodemics is a well-known problem of interest. Nowadays, social media platforms are excellently representing the public sentiments and opinions about current events. Twitter is one of the most popular social media network that has captured the attention of researchers for studying public sentiments. Pandemic and Infodemics prediction on the basis of public sentiments expressed on Twitter has been an intriguing field of research. In this study we are focusing on people who have highest number of followers in Pakistan with most tweets related to Covid-19. The manually annotated dataset contains 2000 tweets of 1000 users for training and 380 tweets for test data from June to July 2020. For data processing we have manually labeled and added features to the dataset with the help of Senti Word Net. In the proposed model, KORONV is collecting data of tweets which will show the hashtags of COVID-19. Multiple machine learning algorithms are applied and the Long Short Term Memory (LSTM) gives the best accuracy of 98%. These techniques will be used to recognize patterns on the basis of existing algorithms, data sets and to develop adequate solution concepts that will be used for identification and classification between positive negative and neutral sentiment classification.