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Aspect based sentence segregated dataset of hybrid car's consumers online reviews

Dataset presented in this paper is obtained from the top online automobile selling and purchasing websites. A total of 1000 reviews related to hybrid cars in the form of text reviews are extracted with the help of the Web Scraper tool. The dataset presents the customers sentiments in the form of rev...

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Detalles Bibliográficos
Autores principales: Manzoor, Muhammad Faraz, Abid, Adnan, Nawaz, Naeem A., Alvi, Atif
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9142620/
https://www.ncbi.nlm.nih.gov/pubmed/35637892
http://dx.doi.org/10.1016/j.dib.2022.108293
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author Manzoor, Muhammad Faraz
Abid, Adnan
Nawaz, Naeem A.
Alvi, Atif
author_facet Manzoor, Muhammad Faraz
Abid, Adnan
Nawaz, Naeem A.
Alvi, Atif
author_sort Manzoor, Muhammad Faraz
collection PubMed
description Dataset presented in this paper is obtained from the top online automobile selling and purchasing websites. A total of 1000 reviews related to hybrid cars in the form of text reviews are extracted with the help of the Web Scraper tool. The dataset presents the customers sentiments in the form of reviews related to hybrid cars. Various aspects are taken into consideration while annotating the reviews such as driving, performance, comfort, safety features, interior, exterior and accessories. The annotation of data is done at three levels by three annotators i.e., (1) overall polarity of a review, (2) segregation of the sentence term in which aspect is discussed, (3) polarity of the discussed aspect. Cohen's Kappa score of 0.90 was achieved among the authors while annotating the reviews. Dataset can be used for sentiment analysis, information retrieving, lexicon analysis, and grammatical and morphological analysis.
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spelling pubmed-91426202022-05-29 Aspect based sentence segregated dataset of hybrid car's consumers online reviews Manzoor, Muhammad Faraz Abid, Adnan Nawaz, Naeem A. Alvi, Atif Data Brief Data Article Dataset presented in this paper is obtained from the top online automobile selling and purchasing websites. A total of 1000 reviews related to hybrid cars in the form of text reviews are extracted with the help of the Web Scraper tool. The dataset presents the customers sentiments in the form of reviews related to hybrid cars. Various aspects are taken into consideration while annotating the reviews such as driving, performance, comfort, safety features, interior, exterior and accessories. The annotation of data is done at three levels by three annotators i.e., (1) overall polarity of a review, (2) segregation of the sentence term in which aspect is discussed, (3) polarity of the discussed aspect. Cohen's Kappa score of 0.90 was achieved among the authors while annotating the reviews. Dataset can be used for sentiment analysis, information retrieving, lexicon analysis, and grammatical and morphological analysis. Elsevier 2022-05-17 /pmc/articles/PMC9142620/ /pubmed/35637892 http://dx.doi.org/10.1016/j.dib.2022.108293 Text en © 2022 The Authors. Published by Elsevier Inc. https://creativecommons.org/licenses/by/4.0/This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Data Article
Manzoor, Muhammad Faraz
Abid, Adnan
Nawaz, Naeem A.
Alvi, Atif
Aspect based sentence segregated dataset of hybrid car's consumers online reviews
title Aspect based sentence segregated dataset of hybrid car's consumers online reviews
title_full Aspect based sentence segregated dataset of hybrid car's consumers online reviews
title_fullStr Aspect based sentence segregated dataset of hybrid car's consumers online reviews
title_full_unstemmed Aspect based sentence segregated dataset of hybrid car's consumers online reviews
title_short Aspect based sentence segregated dataset of hybrid car's consumers online reviews
title_sort aspect based sentence segregated dataset of hybrid car's consumers online reviews
topic Data Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9142620/
https://www.ncbi.nlm.nih.gov/pubmed/35637892
http://dx.doi.org/10.1016/j.dib.2022.108293
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