TY - GEN
T1 - CovidStream
T2 - 7th Annual International Conference on Information Management and Big Data, SIMBig 2020
AU - Baca, Herwin Alayn Huillcen
AU - de Luz Palomino Valdivia, Flor
AU - Atencio, Yalmar Ponce
AU - Ibarra, Manuel J.
AU - Cruz, Mario Aquino
AU - Baca, Melvin Edward Huillcen
N1 - Publisher Copyright:
© 2021, Springer Nature Switzerland AG.
PY - 2021
Y1 - 2021
N2 - Since the beginning of the pandemic caused by Covid-19, the emotions of humanity have evolved abruptly, mainly for policies adopted by the governments of countries. These policies, since they have a high impact on people’s health, need feedback on people’s emotional perception and their connections with entities directly related to emotions, to have relevant information for decision making. Given the global social isolation, emotions have been expressed with higher magnitude in comments on social networks, generating a large amount of data that is a source for various investigations. The objective of this work is to design and adapt an interactive visualization tool called CovidStream, for monitoring the evolution of emotions associated with Covid-19 in Peru, for which Visual Analytics, Deep learning, and Sentiment Analysis techniques are combined. This visualization tool allows showing the evolution of the emotions associated with the Covid-19 and its relationships with three entities: persons, places, and organizations, which have an impact on emotions, all in a temporal space dimension. For the visualization of entities and emotions, Peruvian tweets extracted between January and July 2020 were used, all of them with the hashtag #Covid-19. For the classification of emotions, a recurrent neural network model with LSTM architecture was implemented, taking as training and test data the one proposed by SemEval-2018 Task1, corresponding to Spanish tweets labeled with emotions: anger, fear, joy, and sadness.
AB - Since the beginning of the pandemic caused by Covid-19, the emotions of humanity have evolved abruptly, mainly for policies adopted by the governments of countries. These policies, since they have a high impact on people’s health, need feedback on people’s emotional perception and their connections with entities directly related to emotions, to have relevant information for decision making. Given the global social isolation, emotions have been expressed with higher magnitude in comments on social networks, generating a large amount of data that is a source for various investigations. The objective of this work is to design and adapt an interactive visualization tool called CovidStream, for monitoring the evolution of emotions associated with Covid-19 in Peru, for which Visual Analytics, Deep learning, and Sentiment Analysis techniques are combined. This visualization tool allows showing the evolution of the emotions associated with the Covid-19 and its relationships with three entities: persons, places, and organizations, which have an impact on emotions, all in a temporal space dimension. For the visualization of entities and emotions, Peruvian tweets extracted between January and July 2020 were used, all of them with the hashtag #Covid-19. For the classification of emotions, a recurrent neural network model with LSTM architecture was implemented, taking as training and test data the one proposed by SemEval-2018 Task1, corresponding to Spanish tweets labeled with emotions: anger, fear, joy, and sadness.
KW - Deep learning
KW - Emotion classification
KW - Entity recognition
KW - LSTM
KW - Sentiment analysis
KW - StreamGraph
KW - Time series
KW - Tweets
KW - Visual analytics
KW - Wordcloud
UR - http://www.scopus.com/inward/record.url?scp=85111168483&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-76228-5_39
DO - 10.1007/978-3-030-76228-5_39
M3 - Conference contribution
AN - SCOPUS:85111168483
SN - 9783030762278
T3 - Communications in Computer and Information Science
SP - 540
EP - 551
BT - Information Management and Big Data - 7th Annual International Conference, SIMBig 2020, Proceedings
A2 - Lossio-Ventura, Juan Antonio
A2 - Valverde-Rebaza, Jorge Carlos
A2 - Díaz, Eduardo
A2 - Alatrista-Salas, Hugo
PB - Springer Science and Business Media Deutschland GmbH
Y2 - 1 October 2020 through 3 October 2020
ER -