Twitter Spatio-temporal Topic Dynamics and Sentiment Analysis During the First COVID-19 Lockdown in India

Arunkumar Dhandapani; Anandkumar Balasubramaniam; Thirunavukarasu Balasubramaniam; Anand Paul

doi:10.1007/978-981-19-2347-0_64

Twitter Spatio-temporal Topic Dynamics and Sentiment Analysis During the First COVID-19 Lockdown in India

Arunkumar Dhandapani
, Anandkumar Balasubramaniam
, Thirunavukarasu Balasubramaniam
, Anand Paul

Research output: Chapter in Book/Report/Conference proceeding › Chapter › peer-review

Abstract

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.

Original language	English
Title of host publication	Lecture Notes on Data Engineering and Communications Technologies
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	831-842
Number of pages	12
DOIs	https://doi.org/10.1007/978-981-19-2347-0_64
State	Published - 2022

Publication series

Name	Lecture Notes on Data Engineering and Communications Technologies
Volume	132
ISSN (Print)	2367-4512
ISSN (Electronic)	2367-4520

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Keywords

COVID-19
India
Nonnegative tensor factorization
Sentiment analysis
Topic modelling

Access to Document

10.1007/978-981-19-2347-0_64

Cite this

Dhandapani, A., Balasubramaniam, A., Balasubramaniam, T., & Paul, A. (2022). Twitter Spatio-temporal Topic Dynamics and Sentiment Analysis During the First COVID-19 Lockdown in India. In Lecture Notes on Data Engineering and Communications Technologies (pp. 831-842). (Lecture Notes on Data Engineering and Communications Technologies; Vol. 132). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-981-19-2347-0_64

Dhandapani, Arunkumar ; Balasubramaniam, Anandkumar ; Balasubramaniam, Thirunavukarasu et al. / Twitter Spatio-temporal Topic Dynamics and Sentiment Analysis During the First COVID-19 Lockdown in India. Lecture Notes on Data Engineering and Communications Technologies. Springer Science and Business Media Deutschland GmbH, 2022. pp. 831-842 (Lecture Notes on Data Engineering and Communications Technologies).

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abstract = "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.",

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author = "Arunkumar Dhandapani and Anandkumar Balasubramaniam and Thirunavukarasu Balasubramaniam and Anand Paul",

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Dhandapani, A, Balasubramaniam, A, Balasubramaniam, T & Paul, A 2022, Twitter Spatio-temporal Topic Dynamics and Sentiment Analysis During the First COVID-19 Lockdown in India. in Lecture Notes on Data Engineering and Communications Technologies. Lecture Notes on Data Engineering and Communications Technologies, vol. 132, Springer Science and Business Media Deutschland GmbH, pp. 831-842. https://doi.org/10.1007/978-981-19-2347-0_64

Twitter Spatio-temporal Topic Dynamics and Sentiment Analysis During the First COVID-19 Lockdown in India. / Dhandapani, Arunkumar; Balasubramaniam, Anandkumar; Balasubramaniam, Thirunavukarasu et al.
Lecture Notes on Data Engineering and Communications Technologies. Springer Science and Business Media Deutschland GmbH, 2022. p. 831-842 (Lecture Notes on Data Engineering and Communications Technologies; Vol. 132).

Research output: Chapter in Book/Report/Conference proceeding › Chapter › peer-review

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

PY - 2022

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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.

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KW - Nonnegative tensor factorization

KW - Sentiment analysis

KW - Topic modelling

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Dhandapani A, Balasubramaniam A, Balasubramaniam T, Paul A. Twitter Spatio-temporal Topic Dynamics and Sentiment Analysis During the First COVID-19 Lockdown in India. In Lecture Notes on Data Engineering and Communications Technologies. Springer Science and Business Media Deutschland GmbH. 2022. p. 831-842. (Lecture Notes on Data Engineering and Communications Technologies). doi: 10.1007/978-981-19-2347-0_64

Twitter Spatio-temporal Topic Dynamics and Sentiment Analysis During the First COVID-19 Lockdown in India

Abstract

Publication series

UN SDGs

Keywords

Access to Document

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