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short-paper

Sentiment Analysis on Tweets related to infectious diseases in South America

Authors:

José Antonio García-Díaz,

Óscar Apolinario-Arzube,

José Medina-Moreira,

Harry Luna-Aveiga,

Katty Lagos-Ortiz,

Rafael Valencia-GarcíaAuthors Info & Claims

EATIS '18: Proceedings of the Euro American Conference on Telematics and Information Systems

Article No.: 21, Pages 1 - 5

https://doi.org/10.1145/3293614.3293647

Published: 12 November 2018 Publication History

Abstract

Infectious diseases have a huge social and economic impact. They are caused by pathogenic microorganisms such as bacteria, viruses, parasites or fungi and they can be transmitted, directly or indirectly, from one person to another or from animals to humans (Zoonoses). Nowadays it is very important to detect the infectious diseases as soon as possible to prevent critical problems for the society. In this work we propose an approach for the sentiment classification of tweets related to infectious diseases. This kind of systems could help health professionals to know how society respond to advances in the treatment of these diseases. In addition, a comparison was made of the performance of three classification algorithms (J48, BayesNet, and SMO). The results showed that SMO provides better results than BayesNet and J48 algorithms, obtaining an F-measure of 84.4%.

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Cited By

Kang IMridul MSanders AMa YMunasinghe TGupta ASeneviratne O(2024)Deciphering Crypto TwitterProceedings of the 16th ACM Web Science Conference10.1145/3614419.3644026(331-342)Online publication date: 21-May-2024
https://dl.acm.org/doi/10.1145/3614419.3644026
Li BHutchinson DLim SMacIntyre C(2024)An attention-based hybrid model for spatial and temporal sentiment analysis of COVID-19 related tweets in the contiguous United StatesGeo-spatial Information Science10.1080/10095020.2024.2408343(1-20)Online publication date: 2-Oct-2024
https://doi.org/10.1080/10095020.2024.2408343
Boulesnane AMeshoul SAouissi K(2022)Influenza-like Illness Detection from Arabic Facebook Posts Based on Sentiment Analysis and 1D Convolutional Neural NetworkMathematics10.3390/math1021408910:21(4089)Online publication date: 2-Nov-2022
https://doi.org/10.3390/math10214089
Show More Cited By

Index Terms

Sentiment Analysis on Tweets related to infectious diseases in South America
1. Applied computing
  1. Life and medical sciences
    1. Health care information systems
2. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Sentiment analysis

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Information & Contributors

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Published In

cover image ACM Other conferences

EATIS '18: Proceedings of the Euro American Conference on Telematics and Information Systems

November 2018

297 pages

ISBN:9781450365727

DOI:10.1145/3293614

Co-chairs:
Gabriel Paillard
Federal University of Ceará (UFC), Brazil
,
Francisco Milton Mendes Neto
Rural Federal University of the Semiarid (UFERSA), Brazil
,
Program Chairs:
Emanuel Ferreira Coutinho
Federal University of Ceará (UFC), Brazil
,
Rogério Patrício Chagas do Nascimento
Federal University of Sergipe (UFS), Brazil

Copyright © 2018 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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EATIS: Euro American Association on Telematics and Information Systems

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 12 November 2018

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Short-paper
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Refereed limited

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EATIS '18

EATIS '18: Euro American Conference on Telematics and Information Systems

November 12 - 15, 2018

Fortaleza, Brazil

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Overall Acceptance Rate 17 of 64 submissions, 27%

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Cited By

Kang IMridul MSanders AMa YMunasinghe TGupta ASeneviratne O(2024)Deciphering Crypto TwitterProceedings of the 16th ACM Web Science Conference10.1145/3614419.3644026(331-342)Online publication date: 21-May-2024
https://dl.acm.org/doi/10.1145/3614419.3644026
Li BHutchinson DLim SMacIntyre C(2024)An attention-based hybrid model for spatial and temporal sentiment analysis of COVID-19 related tweets in the contiguous United StatesGeo-spatial Information Science10.1080/10095020.2024.2408343(1-20)Online publication date: 2-Oct-2024
https://doi.org/10.1080/10095020.2024.2408343
Boulesnane AMeshoul SAouissi K(2022)Influenza-like Illness Detection from Arabic Facebook Posts Based on Sentiment Analysis and 1D Convolutional Neural NetworkMathematics10.3390/math1021408910:21(4089)Online publication date: 2-Nov-2022
https://doi.org/10.3390/math10214089
Osorio Angel SPeña Pérez Negrón AEspinoza-Valdez A(2021)Systematic literature review of sentiment analysis in the Spanish languageData Technologies and Applications10.1108/DTA-09-2020-020055:4(461-479)Online publication date: 16-Feb-2021
https://doi.org/10.1108/DTA-09-2020-0200
Apolinardo-Arzube OGarcía-Díaz JMedina-Moreira JLuna-Aveiga HValencia-García R(2019)Evaluating Information-Retrieval Models and Machine-Learning Classifiers for Measuring the Social Perception towards Infectious DiseasesApplied Sciences10.3390/app91428589:14(2858)Online publication date: 18-Jul-2019
https://doi.org/10.3390/app9142858

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