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A reliable sentiment analysis for classification of tweets in social networks
In modern society, the use of social networks is more than ever and they have become the most popular medium for daily communications. Twitter is a social network where users are able to share their daily emotions and opinions with tweets. Sentiment analysis is a method to identify these emotions an...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer Vienna
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9742011/ https://www.ncbi.nlm.nih.gov/pubmed/36532862 http://dx.doi.org/10.1007/s13278-022-00998-2 |
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author | AminiMotlagh, Masoud Shahhoseini, HadiShahriar Fatehi, Nina |
author_facet | AminiMotlagh, Masoud Shahhoseini, HadiShahriar Fatehi, Nina |
author_sort | AminiMotlagh, Masoud |
collection | PubMed |
description | In modern society, the use of social networks is more than ever and they have become the most popular medium for daily communications. Twitter is a social network where users are able to share their daily emotions and opinions with tweets. Sentiment analysis is a method to identify these emotions and determine whether a text is positive, negative, or neutral. In this article, we apply four widely used data mining classifiers, namely K-nearest neighbor, decision tree, support vector machine, and naive Bayes, to analyze the sentiment of the tweets. The analysis is performed on two datasets: first, a dataset with two classes (positive and negative) and then a three-class dataset (positive, negative and neutral). Furthermore, we utilize two ensemble methods to decrease variance and bias of the learning algorithms and subsequently increase the reliability. Also, we have divided the dataset into two parts: training set and testing set with different percentages of data to show the best train–test split ratio. Our results show that support vector machine demonstrates better outcomes compared to other algorithms, showing an improvement of 3.53% on dataset with two-class data and 7.41% on dataset with three-class data in accuracy rate compared to other algorithms. The experiments show that the accuracy of single classifiers slightly outperforms that of ensemble methods; however, they propose more reliable learning models. Results also demonstrate that using 50% of the dataset as training data has almost the same results as 70%, while using tenfold cross-validation can reach better results. |
format | Online Article Text |
id | pubmed-9742011 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Springer Vienna |
record_format | MEDLINE/PubMed |
spelling | pubmed-97420112022-12-12 A reliable sentiment analysis for classification of tweets in social networks AminiMotlagh, Masoud Shahhoseini, HadiShahriar Fatehi, Nina Soc Netw Anal Min Original Article In modern society, the use of social networks is more than ever and they have become the most popular medium for daily communications. Twitter is a social network where users are able to share their daily emotions and opinions with tweets. Sentiment analysis is a method to identify these emotions and determine whether a text is positive, negative, or neutral. In this article, we apply four widely used data mining classifiers, namely K-nearest neighbor, decision tree, support vector machine, and naive Bayes, to analyze the sentiment of the tweets. The analysis is performed on two datasets: first, a dataset with two classes (positive and negative) and then a three-class dataset (positive, negative and neutral). Furthermore, we utilize two ensemble methods to decrease variance and bias of the learning algorithms and subsequently increase the reliability. Also, we have divided the dataset into two parts: training set and testing set with different percentages of data to show the best train–test split ratio. Our results show that support vector machine demonstrates better outcomes compared to other algorithms, showing an improvement of 3.53% on dataset with two-class data and 7.41% on dataset with three-class data in accuracy rate compared to other algorithms. The experiments show that the accuracy of single classifiers slightly outperforms that of ensemble methods; however, they propose more reliable learning models. Results also demonstrate that using 50% of the dataset as training data has almost the same results as 70%, while using tenfold cross-validation can reach better results. Springer Vienna 2022-12-12 2023 /pmc/articles/PMC9742011/ /pubmed/36532862 http://dx.doi.org/10.1007/s13278-022-00998-2 Text en © The Author(s), under exclusive licence to Springer-Verlag GmbH Austria, part of Springer Nature 2022, Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic. |
spellingShingle | Original Article AminiMotlagh, Masoud Shahhoseini, HadiShahriar Fatehi, Nina A reliable sentiment analysis for classification of tweets in social networks |
title | A reliable sentiment analysis for classification of tweets in social networks |
title_full | A reliable sentiment analysis for classification of tweets in social networks |
title_fullStr | A reliable sentiment analysis for classification of tweets in social networks |
title_full_unstemmed | A reliable sentiment analysis for classification of tweets in social networks |
title_short | A reliable sentiment analysis for classification of tweets in social networks |
title_sort | reliable sentiment analysis for classification of tweets in social networks |
topic | Original Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9742011/ https://www.ncbi.nlm.nih.gov/pubmed/36532862 http://dx.doi.org/10.1007/s13278-022-00998-2 |
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