TY - CHAP
T1 - Twitter Spatio-temporal Topic Dynamics and Sentiment Analysis During the First COVID-19 Lockdown in India
AU - Dhandapani, Arunkumar
AU - Balasubramaniam, Anandkumar
AU - Balasubramaniam, Thirunavukarasu
AU - Paul, Anand
N1 - Publisher Copyright:
© 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
PY - 2022
Y1 - 2022
N2 - The advent of COVID-19 has tremendously affected the global economy. More people have suffered, and some of them even lost their lives. As the worst part, many countries are seeing second and third waves of COVID-19 cases despite the vaccination. India is one such country that is worse affected by the COVID-19. Understanding what people of India think and how they express their thoughts through social media platforms like Twitter has a vast significance. Therefore, in this project, we aim to use COVID-19-related tweets during the first COVID-19 lockdown in India and apply the nonnegative tensor factorization (NTF) algorithm to elicit spatio-temporal topic dynamics. While knowing the sentiments of people is important, identifying sentiments for each tweet is time-consuming and hard to interpret. Therefore, in this paper, we propose to apply sentiment analysis on the topics identified using NTF.
AB - The advent of COVID-19 has tremendously affected the global economy. More people have suffered, and some of them even lost their lives. As the worst part, many countries are seeing second and third waves of COVID-19 cases despite the vaccination. India is one such country that is worse affected by the COVID-19. Understanding what people of India think and how they express their thoughts through social media platforms like Twitter has a vast significance. Therefore, in this project, we aim to use COVID-19-related tweets during the first COVID-19 lockdown in India and apply the nonnegative tensor factorization (NTF) algorithm to elicit spatio-temporal topic dynamics. While knowing the sentiments of people is important, identifying sentiments for each tweet is time-consuming and hard to interpret. Therefore, in this paper, we propose to apply sentiment analysis on the topics identified using NTF.
KW - COVID-19
KW - India
KW - Nonnegative tensor factorization
KW - Sentiment analysis
KW - Topic modelling
UR - http://www.scopus.com/inward/record.url?scp=85135514697&partnerID=8YFLogxK
U2 - 10.1007/978-981-19-2347-0_64
DO - 10.1007/978-981-19-2347-0_64
M3 - Chapter
AN - SCOPUS:85135514697
T3 - Lecture Notes on Data Engineering and Communications Technologies
SP - 831
EP - 842
BT - Lecture Notes on Data Engineering and Communications Technologies
PB - Springer Science and Business Media Deutschland GmbH
ER -
