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Comparison of Machine Learning and Sentiment Analysis in Detection of Suspicious Online Reviewers on Different Type of Data
The article focuses on solving an important problem of detecting suspicious reviewers in online discussions on social networks. We have concentrated on a special type of suspicious authors, on trolls. We have used methods of machine learning for generation of detection models to discriminate a troll...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8747373/ https://www.ncbi.nlm.nih.gov/pubmed/35009698 http://dx.doi.org/10.3390/s22010155 |
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author | Machova, Kristina Mach, Marian Vasilko, Matej |
author_facet | Machova, Kristina Mach, Marian Vasilko, Matej |
author_sort | Machova, Kristina |
collection | PubMed |
description | The article focuses on solving an important problem of detecting suspicious reviewers in online discussions on social networks. We have concentrated on a special type of suspicious authors, on trolls. We have used methods of machine learning for generation of detection models to discriminate a troll reviewer from a common reviewer, but also methods of sentiment analysis to recognize the sentiment typical for troll’s comments. The sentiment analysis can be provided also using machine learning or lexicon-based approach. We have used lexicon-based sentiment analysis for its better ability to detect a dictionary typical for troll authors. We have achieved Accuracy = 0.95 and F1 = 0.80 using sentiment analysis. The best results using machine learning methods were achieved by support vector machine, Accuracy = 0.986 and F1 = 0.988, using a dataset with the set of all selected attributes. We can conclude that detection model based on machine learning is more successful than lexicon-based sentiment analysis, but the difference in accuracy is not so large as in F1 measure. |
format | Online Article Text |
id | pubmed-8747373 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-87473732022-01-11 Comparison of Machine Learning and Sentiment Analysis in Detection of Suspicious Online Reviewers on Different Type of Data Machova, Kristina Mach, Marian Vasilko, Matej Sensors (Basel) Article The article focuses on solving an important problem of detecting suspicious reviewers in online discussions on social networks. We have concentrated on a special type of suspicious authors, on trolls. We have used methods of machine learning for generation of detection models to discriminate a troll reviewer from a common reviewer, but also methods of sentiment analysis to recognize the sentiment typical for troll’s comments. The sentiment analysis can be provided also using machine learning or lexicon-based approach. We have used lexicon-based sentiment analysis for its better ability to detect a dictionary typical for troll authors. We have achieved Accuracy = 0.95 and F1 = 0.80 using sentiment analysis. The best results using machine learning methods were achieved by support vector machine, Accuracy = 0.986 and F1 = 0.988, using a dataset with the set of all selected attributes. We can conclude that detection model based on machine learning is more successful than lexicon-based sentiment analysis, but the difference in accuracy is not so large as in F1 measure. MDPI 2021-12-27 /pmc/articles/PMC8747373/ /pubmed/35009698 http://dx.doi.org/10.3390/s22010155 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Machova, Kristina Mach, Marian Vasilko, Matej Comparison of Machine Learning and Sentiment Analysis in Detection of Suspicious Online Reviewers on Different Type of Data |
title | Comparison of Machine Learning and Sentiment Analysis in Detection of Suspicious Online Reviewers on Different Type of Data |
title_full | Comparison of Machine Learning and Sentiment Analysis in Detection of Suspicious Online Reviewers on Different Type of Data |
title_fullStr | Comparison of Machine Learning and Sentiment Analysis in Detection of Suspicious Online Reviewers on Different Type of Data |
title_full_unstemmed | Comparison of Machine Learning and Sentiment Analysis in Detection of Suspicious Online Reviewers on Different Type of Data |
title_short | Comparison of Machine Learning and Sentiment Analysis in Detection of Suspicious Online Reviewers on Different Type of Data |
title_sort | comparison of machine learning and sentiment analysis in detection of suspicious online reviewers on different type of data |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8747373/ https://www.ncbi.nlm.nih.gov/pubmed/35009698 http://dx.doi.org/10.3390/s22010155 |
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