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Analyzing tourism reviews using an LDA topic-based sentiment analysis approach
It has become increasingly necessary to automate systems for organizing and classifying user reviews according to their domain-specific aspects and sentiment polarities, as online customer opinions have increased on specialized platforms and social networks. This study's methodology employs a c...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9672446/ https://www.ncbi.nlm.nih.gov/pubmed/36405368 http://dx.doi.org/10.1016/j.mex.2022.101894 |
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author | Ali, Twil Omar, Bencharef Soulaimane, Kaloun |
author_facet | Ali, Twil Omar, Bencharef Soulaimane, Kaloun |
author_sort | Ali, Twil |
collection | PubMed |
description | It has become increasingly necessary to automate systems for organizing and classifying user reviews according to their domain-specific aspects and sentiment polarities, as online customer opinions have increased on specialized platforms and social networks. This study's methodology employs a combination of topic modeling and sentiment analysis, as well as human validation techniques of topic labels, to extract valuable insights about Marrakech city from TripAdvisor reviews. Through this technique, tourism practitioners and field specialists may dive deeper into online users generated content, leveraging aspect-based sentiment analysis to explore each destination's weaknesses and strengths. • Data collection and pre-processing. • Extracting latent topics using LDA algorithm (Latent Dirichlet Allocation) on collected reviews. • Applying sentiment analysis to each topic. |
format | Online Article Text |
id | pubmed-9672446 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Elsevier |
record_format | MEDLINE/PubMed |
spelling | pubmed-96724462022-11-19 Analyzing tourism reviews using an LDA topic-based sentiment analysis approach Ali, Twil Omar, Bencharef Soulaimane, Kaloun MethodsX Method Article It has become increasingly necessary to automate systems for organizing and classifying user reviews according to their domain-specific aspects and sentiment polarities, as online customer opinions have increased on specialized platforms and social networks. This study's methodology employs a combination of topic modeling and sentiment analysis, as well as human validation techniques of topic labels, to extract valuable insights about Marrakech city from TripAdvisor reviews. Through this technique, tourism practitioners and field specialists may dive deeper into online users generated content, leveraging aspect-based sentiment analysis to explore each destination's weaknesses and strengths. • Data collection and pre-processing. • Extracting latent topics using LDA algorithm (Latent Dirichlet Allocation) on collected reviews. • Applying sentiment analysis to each topic. Elsevier 2022-11-05 /pmc/articles/PMC9672446/ /pubmed/36405368 http://dx.doi.org/10.1016/j.mex.2022.101894 Text en © 2022 The Author(s) 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 | Method Article Ali, Twil Omar, Bencharef Soulaimane, Kaloun Analyzing tourism reviews using an LDA topic-based sentiment analysis approach |
title | Analyzing tourism reviews using an LDA topic-based sentiment analysis approach |
title_full | Analyzing tourism reviews using an LDA topic-based sentiment analysis approach |
title_fullStr | Analyzing tourism reviews using an LDA topic-based sentiment analysis approach |
title_full_unstemmed | Analyzing tourism reviews using an LDA topic-based sentiment analysis approach |
title_short | Analyzing tourism reviews using an LDA topic-based sentiment analysis approach |
title_sort | analyzing tourism reviews using an lda topic-based sentiment analysis approach |
topic | Method Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9672446/ https://www.ncbi.nlm.nih.gov/pubmed/36405368 http://dx.doi.org/10.1016/j.mex.2022.101894 |
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