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SentiHealth: creating health-related sentiment lexicon using hybrid approach
The exponential increase in the health-related online reviews has played a pivotal role in the development of sentiment analysis systems for extracting and analyzing user-generated health reviews about a drug or medication. The existing general purpose opinion lexicons, such as SentiWordNet has a li...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer International Publishing
2016
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4954801/ https://www.ncbi.nlm.nih.gov/pubmed/27504237 http://dx.doi.org/10.1186/s40064-016-2809-x |
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author | Asghar, Muhammad Zubair Ahmad, Shakeel Qasim, Maria Zahra, Syeda Rabail Kundi, Fazal Masud |
author_facet | Asghar, Muhammad Zubair Ahmad, Shakeel Qasim, Maria Zahra, Syeda Rabail Kundi, Fazal Masud |
author_sort | Asghar, Muhammad Zubair |
collection | PubMed |
description | The exponential increase in the health-related online reviews has played a pivotal role in the development of sentiment analysis systems for extracting and analyzing user-generated health reviews about a drug or medication. The existing general purpose opinion lexicons, such as SentiWordNet has a limited coverage of health-related terms, creating problems for the development of health-based sentiment analysis applications. In this work, we present a hybrid approach to create health-related domain specific lexicon for the efficient classification and scoring of health-related users’ sentiments. The proposed approach is based on the bootstrapping modal, a dataset of health reviews, and corpus-based sentiment detection and scoring. In each of the iteration, vocabulary of the lexicon is updated automatically from an initial seed cache, irrelevant words are filtered, words are declared as medical or non-medical entries, and finally sentiment class and score is assigned to each of the word. The results obtained demonstrate the efficacy of the proposed technique. |
format | Online Article Text |
id | pubmed-4954801 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2016 |
publisher | Springer International Publishing |
record_format | MEDLINE/PubMed |
spelling | pubmed-49548012016-08-08 SentiHealth: creating health-related sentiment lexicon using hybrid approach Asghar, Muhammad Zubair Ahmad, Shakeel Qasim, Maria Zahra, Syeda Rabail Kundi, Fazal Masud Springerplus Research The exponential increase in the health-related online reviews has played a pivotal role in the development of sentiment analysis systems for extracting and analyzing user-generated health reviews about a drug or medication. The existing general purpose opinion lexicons, such as SentiWordNet has a limited coverage of health-related terms, creating problems for the development of health-based sentiment analysis applications. In this work, we present a hybrid approach to create health-related domain specific lexicon for the efficient classification and scoring of health-related users’ sentiments. The proposed approach is based on the bootstrapping modal, a dataset of health reviews, and corpus-based sentiment detection and scoring. In each of the iteration, vocabulary of the lexicon is updated automatically from an initial seed cache, irrelevant words are filtered, words are declared as medical or non-medical entries, and finally sentiment class and score is assigned to each of the word. The results obtained demonstrate the efficacy of the proposed technique. Springer International Publishing 2016-07-20 /pmc/articles/PMC4954801/ /pubmed/27504237 http://dx.doi.org/10.1186/s40064-016-2809-x Text en © The Author(s) 2016 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. |
spellingShingle | Research Asghar, Muhammad Zubair Ahmad, Shakeel Qasim, Maria Zahra, Syeda Rabail Kundi, Fazal Masud SentiHealth: creating health-related sentiment lexicon using hybrid approach |
title | SentiHealth: creating health-related sentiment lexicon using hybrid approach |
title_full | SentiHealth: creating health-related sentiment lexicon using hybrid approach |
title_fullStr | SentiHealth: creating health-related sentiment lexicon using hybrid approach |
title_full_unstemmed | SentiHealth: creating health-related sentiment lexicon using hybrid approach |
title_short | SentiHealth: creating health-related sentiment lexicon using hybrid approach |
title_sort | sentihealth: creating health-related sentiment lexicon using hybrid approach |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4954801/ https://www.ncbi.nlm.nih.gov/pubmed/27504237 http://dx.doi.org/10.1186/s40064-016-2809-x |
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