Sentiment Analysis of Tweets Mentioning Research Articles in Medicine and Psychiatry Disciplines

Sampathkumar Kuppan Bharathwaj; Jin Cheon Na; Babu Sangeetha; Eswaran Sarathkumar

doi:10.1007/978-3-030-34058-2_29

Sentiment Analysis of Tweets Mentioning Research Articles in Medicine and Psychiatry Disciplines

Sampathkumar Kuppan Bharathwaj, Jin Cheon Na^*, Babu Sangeetha, Eswaran Sarathkumar

^*Corresponding author for this work

Nanyang Technological University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

1 Citation (Scopus)

Abstract

Recently altmetrics (short for alternative metrics) are gaining popularity among researchers to identify the impact of scholarly publications among the general public. Although altmetrics have been widely used nowadays, there has been a limited number of studies analyzing users’ sentiments towards these scholarly publications on social media platforms. In this paper, we analyzed and compared user sentiments (positive, negative and neutral) towards scholarly publications in Medicine and Psychiatry domains by analyzing user-generated content (tweets) on Twitter. We explored various machine learning algorithms, and constructed the best model with Support Vector Machine (SVM) which gave an accuracy of 91.6%.

Original language	English
Title of host publication	Digital Libraries at the Crossroads of Digital Information for the Future - 21st International Conference on Asia-Pacific Digital Libraries, ICADL 2019, Proceedings
Editors	Adam Jatowt, Akira Maeda, Sue Yeon Syn
Publisher	Springer
Pages	303-307
Number of pages	5
ISBN (Print)	9783030340575
DOIs	https://doi.org/10.1007/978-3-030-34058-2_29
Publication status	Published - 2019
Externally published	Yes
Event	21st International Conference on Asia-Pacific Digital Libraries, ICADL 2019 - Kuala Lumpur, Malaysia Duration: Nov 4 2019 → Nov 7 2019

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	11853 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	21st International Conference on Asia-Pacific Digital Libraries, ICADL 2019
Country/Territory	Malaysia
City	Kuala Lumpur
Period	11/4/19 → 11/7/19

Bibliographical note

Publisher Copyright:
© Springer Nature Switzerland AG, 2019.

ASJC Scopus Subject Areas

Theoretical Computer Science
General Computer Science

Keywords

Altmetrics
Medicine
Psychiatry
Sentiment analysis
Text classification
Twitter

Access to Document

10.1007/978-3-030-34058-2_29

Cite this

Bharathwaj, S. K., Na, J. C., Sangeetha, B., & Sarathkumar, E. (2019). Sentiment Analysis of Tweets Mentioning Research Articles in Medicine and Psychiatry Disciplines. In A. Jatowt, A. Maeda, & S. Y. Syn (Eds.), Digital Libraries at the Crossroads of Digital Information for the Future - 21st International Conference on Asia-Pacific Digital Libraries, ICADL 2019, Proceedings (pp. 303-307). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11853 LNCS). Springer. https://doi.org/10.1007/978-3-030-34058-2_29

Bharathwaj, Sampathkumar Kuppan ; Na, Jin Cheon ; Sangeetha, Babu et al. / Sentiment Analysis of Tweets Mentioning Research Articles in Medicine and Psychiatry Disciplines. Digital Libraries at the Crossroads of Digital Information for the Future - 21st International Conference on Asia-Pacific Digital Libraries, ICADL 2019, Proceedings. editor / Adam Jatowt ; Akira Maeda ; Sue Yeon Syn. Springer, 2019. pp. 303-307 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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title = "Sentiment Analysis of Tweets Mentioning Research Articles in Medicine and Psychiatry Disciplines",

abstract = "Recently altmetrics (short for alternative metrics) are gaining popularity among researchers to identify the impact of scholarly publications among the general public. Although altmetrics have been widely used nowadays, there has been a limited number of studies analyzing users{\textquoteright} sentiments towards these scholarly publications on social media platforms. In this paper, we analyzed and compared user sentiments (positive, negative and neutral) towards scholarly publications in Medicine and Psychiatry domains by analyzing user-generated content (tweets) on Twitter. We explored various machine learning algorithms, and constructed the best model with Support Vector Machine (SVM) which gave an accuracy of 91.6\%.",

keywords = "Altmetrics, Medicine, Psychiatry, Sentiment analysis, Text classification, Twitter",

author = "Bharathwaj, \{Sampathkumar Kuppan\} and Na, \{Jin Cheon\} and Babu Sangeetha and Eswaran Sarathkumar",

note = "Publisher Copyright: {\textcopyright} Springer Nature Switzerland AG, 2019.; 21st International Conference on Asia-Pacific Digital Libraries, ICADL 2019 ; Conference date: 04-11-2019 Through 07-11-2019",

year = "2019",

doi = "10.1007/978-3-030-34058-2\_29",

language = "English",

isbn = "9783030340575",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

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Bharathwaj, SK, Na, JC, Sangeetha, B & Sarathkumar, E 2019, Sentiment Analysis of Tweets Mentioning Research Articles in Medicine and Psychiatry Disciplines. in A Jatowt, A Maeda & SY Syn (eds), Digital Libraries at the Crossroads of Digital Information for the Future - 21st International Conference on Asia-Pacific Digital Libraries, ICADL 2019, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 11853 LNCS, Springer, pp. 303-307, 21st International Conference on Asia-Pacific Digital Libraries, ICADL 2019, Kuala Lumpur, Malaysia, 11/4/19. https://doi.org/10.1007/978-3-030-34058-2_29

Sentiment Analysis of Tweets Mentioning Research Articles in Medicine and Psychiatry Disciplines. / Bharathwaj, Sampathkumar Kuppan; Na, Jin Cheon; Sangeetha, Babu et al.
Digital Libraries at the Crossroads of Digital Information for the Future - 21st International Conference on Asia-Pacific Digital Libraries, ICADL 2019, Proceedings. ed. / Adam Jatowt; Akira Maeda; Sue Yeon Syn. Springer, 2019. p. 303-307 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11853 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - Sentiment Analysis of Tweets Mentioning Research Articles in Medicine and Psychiatry Disciplines

AU - Bharathwaj, Sampathkumar Kuppan

AU - Na, Jin Cheon

AU - Sangeetha, Babu

AU - Sarathkumar, Eswaran

N1 - Publisher Copyright: © Springer Nature Switzerland AG, 2019.

PY - 2019

Y1 - 2019

N2 - Recently altmetrics (short for alternative metrics) are gaining popularity among researchers to identify the impact of scholarly publications among the general public. Although altmetrics have been widely used nowadays, there has been a limited number of studies analyzing users’ sentiments towards these scholarly publications on social media platforms. In this paper, we analyzed and compared user sentiments (positive, negative and neutral) towards scholarly publications in Medicine and Psychiatry domains by analyzing user-generated content (tweets) on Twitter. We explored various machine learning algorithms, and constructed the best model with Support Vector Machine (SVM) which gave an accuracy of 91.6%.

AB - Recently altmetrics (short for alternative metrics) are gaining popularity among researchers to identify the impact of scholarly publications among the general public. Although altmetrics have been widely used nowadays, there has been a limited number of studies analyzing users’ sentiments towards these scholarly publications on social media platforms. In this paper, we analyzed and compared user sentiments (positive, negative and neutral) towards scholarly publications in Medicine and Psychiatry domains by analyzing user-generated content (tweets) on Twitter. We explored various machine learning algorithms, and constructed the best model with Support Vector Machine (SVM) which gave an accuracy of 91.6%.

KW - Altmetrics

KW - Medicine

KW - Psychiatry

KW - Sentiment analysis

KW - Text classification

KW - Twitter

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T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

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BT - Digital Libraries at the Crossroads of Digital Information for the Future - 21st International Conference on Asia-Pacific Digital Libraries, ICADL 2019, Proceedings

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PB - Springer

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Y2 - 4 November 2019 through 7 November 2019

ER -

Bharathwaj SK, Na JC, Sangeetha B, Sarathkumar E. Sentiment Analysis of Tweets Mentioning Research Articles in Medicine and Psychiatry Disciplines. In Jatowt A, Maeda A, Syn SY, editors, Digital Libraries at the Crossroads of Digital Information for the Future - 21st International Conference on Asia-Pacific Digital Libraries, ICADL 2019, Proceedings. Springer. 2019. p. 303-307. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-34058-2_29

Sentiment Analysis of Tweets Mentioning Research Articles in Medicine and Psychiatry Disciplines

Abstract

Publication series

Conference

Bibliographical note

ASJC Scopus Subject Areas

Keywords

Access to Document

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