Multi-label Emotion Classification of COVID–19 Tweets with Deep Learning and Topic Modelling  

作　　者：K.Anuratha M.Parvathy 

机构地区：[1]Department of Information Technology,Sri Sai Ram Institute of Technology,Chennai,Tamilnadu,India [2]Department of Computer Science and Engineering,Sethu Institute of Technology,Madurai,Tamilnadu,India

出　　处：《Computer Systems Science & Engineering》2023年第6期3005-3021,共17页计算机系统科学与工程（英文）

摘　　要：The COVID-19 pandemic has become one of the severe diseases in recent years.As it majorly affects the common livelihood of people across the universe,it is essential for administrators and healthcare professionals to be aware of the views of the community so as to monitor the severity of the spread of the outbreak.The public opinions are been shared enormously in microblogging med-ia like twitter and is considered as one of the popular sources to collect public opinions in any topic like politics,sports,entertainment etc.,This work presents a combination of Intensity Based Emotion Classification Convolution Neural Net-work(IBEC-CNN)model and Non-negative Matrix Factorization(NMF)for detecting and analyzing the different topics discussed in the COVID-19 tweets as well the intensity of the emotional content of those tweets.The topics were identified using NMF and the emotions are classified using pretrained IBEC-CNN,based on predefined intensity scores.The research aimed at identifying the emotions in the Indian tweets related to COVID-19 and producing a list of topics discussed by the users during the COVID-19 pandemic.Using the Twitter Application Programming Interface(Twitter API),huge numbers of COVID-19 tweets are retrieved during January and July 2020.The extracted tweets are ana-lyzed for emotions fear,joy,sadness and trust with proposed Intensity Based Emotion Classification Convolution Neural Network(IBEC-CNN)model which is pretrained.The classified tweets are given an intensity score varies from 1 to 3,with 1 being low intensity for the emotion,2 being the moderate and 3 being the high intensity.To identify the topics in the tweets and the themes of those topics,Non-negative Matrix Factorization(NMF)has been employed.Analysis of emotions of COVID-19 tweets has identified,that the count of positive tweets is more than that of count of negative tweets during the period considered and the negative tweets related to COVID-19 is less than 5%.Also,more than 75%nega-tive tweets expressed sadness,fear are of low

关 键 词：TWITTER topic detection emotion classification COVID-19 corona virus non-negative matrix factorization(NMF) convolutional neural network(CNN) sentiment classification healthcare 

分 类 号：R563.1[医药卫生—呼吸系统]