Open AccessDeep spatio-temporal emotion analysis of geo-tagged tweets for predicting location based communal emotion during COVID-19 Lock-down
Author(s) -
M Amsaprabhaa,
Y. Nancy Jane,
H. Khanehemiah
Publication year - 2021
Publication title -
journal of intelligent and fuzzy systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.331
H-Index - 57
eISSN - 1875-8967
pISSN - 1064-1246
DOI - 10.3233/jifs-210544
Subject(s) - computer science , latent dirichlet allocation , sentiment analysis , artificial intelligence , recurrent neural network , grid , covid-19 , globe , machine learning , topic model , artificial neural network , geography , psychology , medicine , disease , geodesy , pathology , neuroscience , infectious disease (medical specialty)
Due to the COVID-19 pandemic, countries across the globe has enforced lockdown restrictions that influence the people’s socio-economic lifecycle. The objective of this paper is to predict the communal emotion of people from different locations during the COVID-19 lockdown. The proposed work aims in developing a deep spatio-temporal analysis framework of geo-tagged tweets to predict the emotions of different topics based on location. An optimized Latent Dirichlet Allocation (LDA) approach is presented for finding the optimal hyper-parameters using grid search. A multi-class emotion classification model is then built via a Recurrent Neural Network (RNN) to predict emotions for each topic based on locations. The proposed work is experimented with the twitter streaming API dataset. The experimental results prove that the presented LDA model-using grid search along with the RNN model for emotion classification outperforms the other state of art methods with an improved accuracy of 94.6%.