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Adaptive sentiment analysis using multioutput classification: a performance comparison
The primary objective of this research is to create a multi-output classification model for sentiment analysis through the combination of 10 algorithms: BernoulliNB, Decision Tree, K-nearest neighbor, Logistic Regression, LinearSVC, Bagging, Stacking, Random Forest, AdaBoost, and ExtraTrees. In doin...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
PeerJ Inc.
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10280487/ https://www.ncbi.nlm.nih.gov/pubmed/37346589 http://dx.doi.org/10.7717/peerj-cs.1378 |
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author | Hariguna, Taqwa Ruangkanjanases, Athapol |
author_facet | Hariguna, Taqwa Ruangkanjanases, Athapol |
author_sort | Hariguna, Taqwa |
collection | PubMed |
description | The primary objective of this research is to create a multi-output classification model for sentiment analysis through the combination of 10 algorithms: BernoulliNB, Decision Tree, K-nearest neighbor, Logistic Regression, LinearSVC, Bagging, Stacking, Random Forest, AdaBoost, and ExtraTrees. In doing so, we aim to identify the optimal algorithm performance and role within the model. The data utilized in this study is derived from customer reviews of cryptocurrencies in Indonesia. Our results indicate that LinearSVC and Stacking exhibit a high accuracy (90%) compared to the other eight algorithms. The resulting multi-output model demonstrates an average accuracy of 88%, which can be considered satisfactory. This research endeavors to innovate in adaptive sentiment analysis classification by developing a multi-output model that utilizes a combination of 10 classification algorithms. |
format | Online Article Text |
id | pubmed-10280487 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | PeerJ Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-102804872023-06-21 Adaptive sentiment analysis using multioutput classification: a performance comparison Hariguna, Taqwa Ruangkanjanases, Athapol PeerJ Comput Sci Data Mining and Machine Learning The primary objective of this research is to create a multi-output classification model for sentiment analysis through the combination of 10 algorithms: BernoulliNB, Decision Tree, K-nearest neighbor, Logistic Regression, LinearSVC, Bagging, Stacking, Random Forest, AdaBoost, and ExtraTrees. In doing so, we aim to identify the optimal algorithm performance and role within the model. The data utilized in this study is derived from customer reviews of cryptocurrencies in Indonesia. Our results indicate that LinearSVC and Stacking exhibit a high accuracy (90%) compared to the other eight algorithms. The resulting multi-output model demonstrates an average accuracy of 88%, which can be considered satisfactory. This research endeavors to innovate in adaptive sentiment analysis classification by developing a multi-output model that utilizes a combination of 10 classification algorithms. PeerJ Inc. 2023-05-09 /pmc/articles/PMC10280487/ /pubmed/37346589 http://dx.doi.org/10.7717/peerj-cs.1378 Text en ©2023 Hariguna and Ruangkanjanases https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ Computer Science) and either DOI or URL of the article must be cited. |
spellingShingle | Data Mining and Machine Learning Hariguna, Taqwa Ruangkanjanases, Athapol Adaptive sentiment analysis using multioutput classification: a performance comparison |
title | Adaptive sentiment analysis using multioutput classification: a performance comparison |
title_full | Adaptive sentiment analysis using multioutput classification: a performance comparison |
title_fullStr | Adaptive sentiment analysis using multioutput classification: a performance comparison |
title_full_unstemmed | Adaptive sentiment analysis using multioutput classification: a performance comparison |
title_short | Adaptive sentiment analysis using multioutput classification: a performance comparison |
title_sort | adaptive sentiment analysis using multioutput classification: a performance comparison |
topic | Data Mining and Machine Learning |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10280487/ https://www.ncbi.nlm.nih.gov/pubmed/37346589 http://dx.doi.org/10.7717/peerj-cs.1378 |
work_keys_str_mv | AT harigunataqwa adaptivesentimentanalysisusingmultioutputclassificationaperformancecomparison AT ruangkanjanasesathapol adaptivesentimentanalysisusingmultioutputclassificationaperformancecomparison |