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OpExHAN: opinion extraction using hierarchical attention network from unstructured reviews
In recent decades, sellers and merchants have asked their customers to share their opinion on the products at online marketplace. Analysis of the massive amounts of reviews for a potential customer is an immense challenge, to decide whether to purchase a product or not. In this paper, a hierarchical...
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/PMC9534471/ https://www.ncbi.nlm.nih.gov/pubmed/36217360 http://dx.doi.org/10.1007/s13278-022-00971-z |
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author | Ratmele, Ankur Thakur, Ramesh |
author_facet | Ratmele, Ankur Thakur, Ramesh |
author_sort | Ratmele, Ankur |
collection | PubMed |
description | In recent decades, sellers and merchants have asked their customers to share their opinion on the products at online marketplace. Analysis of the massive amounts of reviews for a potential customer is an immense challenge, to decide whether to purchase a product or not. In this paper, a hierarchical attention network-based framework is presented to resolve this challenge. In this proposed framework, the Amazon’s Smartphone review dataset is preprocessed using NLP approaches and then applied Glove embedding to extract word vector representation of reviews which identifies contextual information of words. These word vectors are fed into the hierarchical attention network, which produces vectors at the word and sentence levels. Bi-GRU model encodes the words and sentences into hidden vectors. Finally, reviews are classified into five opinion classes like extremely positive, positive, extremely negative, negative and neutral. Furthermore, to perform the experiments with the proposed method, the dataset is divided into three parts such as 80% train, 10% validation and 10% test. Experiments reveal that the proposed framework outperforms baseline methods in terms of accuracy, precision and recall. The OpExHAN model achieved admirable results like 94.6% accuracy, 91% both precision and recall after a lot of hyper-parameter experimentation. |
format | Online Article Text |
id | pubmed-9534471 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Springer Vienna |
record_format | MEDLINE/PubMed |
spelling | pubmed-95344712022-10-06 OpExHAN: opinion extraction using hierarchical attention network from unstructured reviews Ratmele, Ankur Thakur, Ramesh Soc Netw Anal Min Original Article In recent decades, sellers and merchants have asked their customers to share their opinion on the products at online marketplace. Analysis of the massive amounts of reviews for a potential customer is an immense challenge, to decide whether to purchase a product or not. In this paper, a hierarchical attention network-based framework is presented to resolve this challenge. In this proposed framework, the Amazon’s Smartphone review dataset is preprocessed using NLP approaches and then applied Glove embedding to extract word vector representation of reviews which identifies contextual information of words. These word vectors are fed into the hierarchical attention network, which produces vectors at the word and sentence levels. Bi-GRU model encodes the words and sentences into hidden vectors. Finally, reviews are classified into five opinion classes like extremely positive, positive, extremely negative, negative and neutral. Furthermore, to perform the experiments with the proposed method, the dataset is divided into three parts such as 80% train, 10% validation and 10% test. Experiments reveal that the proposed framework outperforms baseline methods in terms of accuracy, precision and recall. The OpExHAN model achieved admirable results like 94.6% accuracy, 91% both precision and recall after a lot of hyper-parameter experimentation. Springer Vienna 2022-10-05 2022 /pmc/articles/PMC9534471/ /pubmed/36217360 http://dx.doi.org/10.1007/s13278-022-00971-z Text en © The Author(s), under exclusive licence to Springer-Verlag GmbH Austria, part of Springer Nature 2022, Springer Nature or its licensor 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 Ratmele, Ankur Thakur, Ramesh OpExHAN: opinion extraction using hierarchical attention network from unstructured reviews |
title | OpExHAN: opinion extraction using hierarchical attention network from unstructured reviews |
title_full | OpExHAN: opinion extraction using hierarchical attention network from unstructured reviews |
title_fullStr | OpExHAN: opinion extraction using hierarchical attention network from unstructured reviews |
title_full_unstemmed | OpExHAN: opinion extraction using hierarchical attention network from unstructured reviews |
title_short | OpExHAN: opinion extraction using hierarchical attention network from unstructured reviews |
title_sort | opexhan: opinion extraction using hierarchical attention network from unstructured reviews |
topic | Original Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9534471/ https://www.ncbi.nlm.nih.gov/pubmed/36217360 http://dx.doi.org/10.1007/s13278-022-00971-z |
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