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2018 | OriginalPaper | Buchkapitel

Sentiment Polarity Detection in Social Networks: An Approach for Asthma Disease Management

verfasst von : Harry Luna-Aveiga, José Medina-Moreira, Katty Lagos-Ortiz, Oscar Apolinario, Mario Andrés Paredes-Valverde, María del Pilar Salas-Zárate, Rafael Valencia-García

Erschienen in: Advanced Computational Methods for Knowledge Engineering

Verlag: Springer International Publishing

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Abstract

Asthma disease is a serious health problem that affects all age groups. Asthma-related hospitalizations and deaths have declined in some countries. However, the number of patients with symptoms has increased in the last years. Even though asthma patients have contact with health professionals, they must be an active part in treatment team. On the other hand, there has been an exponential growth of information about healthcare and diseases management on social networks such as Twitter. Aiming to benefit from this information, in this work we propose a method for detecting the emotional reaction of patients about asthma domain concepts such as physical activities, drugs, among others. The findings obtained from the analysis of such information can help to other patients to avoid habits that could harm their health. Our proposal was evaluated with a corpus of Twitter messages obtaining a precision of 82.95%, a recall of 82.27%, and F-measure of 82.36% in sentiment polarity identification.

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Vorheriges Kapitel A Deep Architecture for Sentiment Analysis of News Articles
Nächstes Kapitel An Overview of Information Discovery Using Latent Semantic Indexing
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Metadaten
Titel
Sentiment Polarity Detection in Social Networks: An Approach for Asthma Disease Management
verfasst von
Harry Luna-Aveiga
José Medina-Moreira
Katty Lagos-Ortiz
Oscar Apolinario
Mario Andrés Paredes-Valverde
María del Pilar Salas-Zárate
Rafael Valencia-García
Copyright-Jahr
2018
Buch
Advanced Computational Methods for Knowledge Engineering

Print ISBN: 978-3-319-61910-1

Electronic ISBN: 978-3-319-61911-8

Copyright-Jahr: 2018

DOI
https://doi.org/10.1007/978-3-319-61911-8_13

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