Embedding knowledge on ontology into the corpus by topic to improve the performance of deep learning methods in sentiment analysis
Sentiment classification, which uses deep learning algorithms, has achieved good results when tested with popular datasets. However, it will be challenging to build a corpus on new topics to train machine learning algorithms in sentiment classification with high confidence. This study proposes a met...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8651669/ https://www.ncbi.nlm.nih.gov/pubmed/34876635 http://dx.doi.org/10.1038/s41598-021-03011-6 |
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author | Nguyen, Duy Ngoc Phan, Tuoi Thi Do, Phuc |
author_facet | Nguyen, Duy Ngoc Phan, Tuoi Thi Do, Phuc |
author_sort | Nguyen, Duy Ngoc |
collection | PubMed |
description | Sentiment classification, which uses deep learning algorithms, has achieved good results when tested with popular datasets. However, it will be challenging to build a corpus on new topics to train machine learning algorithms in sentiment classification with high confidence. This study proposes a method that processes embedding knowledge in the ontology of opinion datasets called knowledge processing and representation based on ontology (KPRO) to represent the significant features of the dataset into the word embedding layer of deep learning algorithms in sentiment classification. Unlike the methods that lexical encode or add information to the corpus, this method adds presentation of raw data based on the expert’s knowledge in the ontology. Once the data has a rich knowledge of the topic, the efficiency of the machine learning algorithms is significantly enhanced. Thus, this method is appliable to embed knowledge in datasets in other languages. The test results show that deep learning methods achieved considerably higher accuracy when trained with the KPRO method’s dataset than when trained with datasets not processed by this method. Therefore, this method is a novel approach to improve the accuracy of deep learning algorithms and increase the reliability of new datasets, thus making them ready for mining. |
format | Online Article Text |
id | pubmed-8651669 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-86516692021-12-08 Embedding knowledge on ontology into the corpus by topic to improve the performance of deep learning methods in sentiment analysis Nguyen, Duy Ngoc Phan, Tuoi Thi Do, Phuc Sci Rep Article Sentiment classification, which uses deep learning algorithms, has achieved good results when tested with popular datasets. However, it will be challenging to build a corpus on new topics to train machine learning algorithms in sentiment classification with high confidence. This study proposes a method that processes embedding knowledge in the ontology of opinion datasets called knowledge processing and representation based on ontology (KPRO) to represent the significant features of the dataset into the word embedding layer of deep learning algorithms in sentiment classification. Unlike the methods that lexical encode or add information to the corpus, this method adds presentation of raw data based on the expert’s knowledge in the ontology. Once the data has a rich knowledge of the topic, the efficiency of the machine learning algorithms is significantly enhanced. Thus, this method is appliable to embed knowledge in datasets in other languages. The test results show that deep learning methods achieved considerably higher accuracy when trained with the KPRO method’s dataset than when trained with datasets not processed by this method. Therefore, this method is a novel approach to improve the accuracy of deep learning algorithms and increase the reliability of new datasets, thus making them ready for mining. Nature Publishing Group UK 2021-12-07 /pmc/articles/PMC8651669/ /pubmed/34876635 http://dx.doi.org/10.1038/s41598-021-03011-6 Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Nguyen, Duy Ngoc Phan, Tuoi Thi Do, Phuc Embedding knowledge on ontology into the corpus by topic to improve the performance of deep learning methods in sentiment analysis |
title | Embedding knowledge on ontology into the corpus by topic to improve the performance of deep learning methods in sentiment analysis |
title_full | Embedding knowledge on ontology into the corpus by topic to improve the performance of deep learning methods in sentiment analysis |
title_fullStr | Embedding knowledge on ontology into the corpus by topic to improve the performance of deep learning methods in sentiment analysis |
title_full_unstemmed | Embedding knowledge on ontology into the corpus by topic to improve the performance of deep learning methods in sentiment analysis |
title_short | Embedding knowledge on ontology into the corpus by topic to improve the performance of deep learning methods in sentiment analysis |
title_sort | embedding knowledge on ontology into the corpus by topic to improve the performance of deep learning methods in sentiment analysis |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8651669/ https://www.ncbi.nlm.nih.gov/pubmed/34876635 http://dx.doi.org/10.1038/s41598-021-03011-6 |
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