Opinion Texts Clustering Using Manifold Learning Based on Sentiment and Semantics Analysis

Nowadays, opinion texts are quickly published on websites and social networks by various users in the form of short texts and also in high volumes and various fields. Because these texts reflect the opinions of many users, their processing and analysis, such as clustering, can be very useful in a va...

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Autores principales:	Sajjad Jahanbakhsh Gudakahriz, Amir Masoud Eftekhari Moghadam, Fariborz Mahmoudi
Formato:	article
Lenguaje:	EN
Publicado:	Hindawi Limited 2021
Materias:	Computer software QA76.75-76.765
Acceso en línea:	https://doaj.org/article/2bac03964aa64713930ad42b8d6e2e7f
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id	oai:doaj.org-article:2bac03964aa64713930ad42b8d6e2e7f
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spelling	oai:doaj.org-article:2bac03964aa64713930ad42b8d6e2e7f2021-11-08T02:35:29ZOpinion Texts Clustering Using Manifold Learning Based on Sentiment and Semantics Analysis1875-919X10.1155/2021/7842631https://doaj.org/article/2bac03964aa64713930ad42b8d6e2e7f2021-01-01T00:00:00Zhttp://dx.doi.org/10.1155/2021/7842631https://doaj.org/toc/1875-919XNowadays, opinion texts are quickly published on websites and social networks by various users in the form of short texts and also in high volumes and various fields. Because these texts reflect the opinions of many users, their processing and analysis, such as clustering, can be very useful in a variety of applications including politics, industry, commerce, and economics. High dimensions of the text representation decrease efficiency of clustering, and an effective solution for this challenge is reducing dimensions of texts. Manifold learning is a powerful tool for nonlinear dimension reduction of high-dimensional data. Therefore, in this paper, for increasing efficiency of opinion texts clustering, by manifold learning, dimensions of the represented opinion texts are reduced based on sentiment and semantics, and their intrinsic dimensions are extracted. Then, the clustering algorithm is applied to dimension-reduced opinion texts. The proposed approach helps us to cluster opinion texts with simultaneous consideration of sentiment and semantics, which has received very little attention in the previous works. This type of clustering helps users of opinion texts to obtain more useful information from texts and also provides more accurate summaries in applications, such as the summarization of opinion texts. Experimental results on three datasets show better performance of the proposed approach on opinion texts in terms of important measures for evaluating clustering efficiency. An improvement of about 9% is observed in terms of accuracy on the third dataset and clustering based on sentiment and semantics.Sajjad Jahanbakhsh GudakahrizAmir Masoud Eftekhari MoghadamFariborz MahmoudiHindawi LimitedarticleComputer softwareQA76.75-76.765ENScientific Programming, Vol 2021 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Computer software QA76.75-76.765
spellingShingle	Computer software QA76.75-76.765 Sajjad Jahanbakhsh Gudakahriz Amir Masoud Eftekhari Moghadam Fariborz Mahmoudi Opinion Texts Clustering Using Manifold Learning Based on Sentiment and Semantics Analysis
description	Nowadays, opinion texts are quickly published on websites and social networks by various users in the form of short texts and also in high volumes and various fields. Because these texts reflect the opinions of many users, their processing and analysis, such as clustering, can be very useful in a variety of applications including politics, industry, commerce, and economics. High dimensions of the text representation decrease efficiency of clustering, and an effective solution for this challenge is reducing dimensions of texts. Manifold learning is a powerful tool for nonlinear dimension reduction of high-dimensional data. Therefore, in this paper, for increasing efficiency of opinion texts clustering, by manifold learning, dimensions of the represented opinion texts are reduced based on sentiment and semantics, and their intrinsic dimensions are extracted. Then, the clustering algorithm is applied to dimension-reduced opinion texts. The proposed approach helps us to cluster opinion texts with simultaneous consideration of sentiment and semantics, which has received very little attention in the previous works. This type of clustering helps users of opinion texts to obtain more useful information from texts and also provides more accurate summaries in applications, such as the summarization of opinion texts. Experimental results on three datasets show better performance of the proposed approach on opinion texts in terms of important measures for evaluating clustering efficiency. An improvement of about 9% is observed in terms of accuracy on the third dataset and clustering based on sentiment and semantics.
format	article
author	Sajjad Jahanbakhsh Gudakahriz Amir Masoud Eftekhari Moghadam Fariborz Mahmoudi
author_facet	Sajjad Jahanbakhsh Gudakahriz Amir Masoud Eftekhari Moghadam Fariborz Mahmoudi
author_sort	Sajjad Jahanbakhsh Gudakahriz
title	Opinion Texts Clustering Using Manifold Learning Based on Sentiment and Semantics Analysis
title_short	Opinion Texts Clustering Using Manifold Learning Based on Sentiment and Semantics Analysis
title_full	Opinion Texts Clustering Using Manifold Learning Based on Sentiment and Semantics Analysis
title_fullStr	Opinion Texts Clustering Using Manifold Learning Based on Sentiment and Semantics Analysis
title_full_unstemmed	Opinion Texts Clustering Using Manifold Learning Based on Sentiment and Semantics Analysis
title_sort	opinion texts clustering using manifold learning based on sentiment and semantics analysis
publisher	Hindawi Limited
publishDate	2021
url	https://doaj.org/article/2bac03964aa64713930ad42b8d6e2e7f
work_keys_str_mv	AT sajjadjahanbakhshgudakahriz opiniontextsclusteringusingmanifoldlearningbasedonsentimentandsemanticsanalysis AT amirmasoudeftekharimoghadam opiniontextsclusteringusingmanifoldlearningbasedonsentimentandsemanticsanalysis AT fariborzmahmoudi opiniontextsclusteringusingmanifoldlearningbasedonsentimentandsemanticsanalysis
_version_	1718443169041874944

Opinion Texts Clustering Using Manifold Learning Based on Sentiment and Semantics Analysis

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