Movies Emotional Analysis Using Textual Contents

In this paper, we use movies and series subtitles and applied text mining and Natural Language Processing methods to evaluate emotions in videos. Three different word lexicons were used and one of the outcomes of this research is the generation of a secondary dataset with more than 3658 records whic...

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Autores principales: Kayhani, Amir Kazem, Meziane, Farid, Chiky, Raja
Formato: Online Artículo Texto
Lenguaje:English
Publicado: 2020
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7298173/
http://dx.doi.org/10.1007/978-3-030-51310-8_19
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author Kayhani, Amir Kazem
Meziane, Farid
Chiky, Raja
author_facet Kayhani, Amir Kazem
Meziane, Farid
Chiky, Raja
author_sort Kayhani, Amir Kazem
collection PubMed
description In this paper, we use movies and series subtitles and applied text mining and Natural Language Processing methods to evaluate emotions in videos. Three different word lexicons were used and one of the outcomes of this research is the generation of a secondary dataset with more than 3658 records which can be used for other data analysis and data mining research. We used our secondary dataset to find and display correlations between different emotions on the videos and the correlation between emotions on the movies and users’ scores on IMDb using the Pearson correlation method and found some statistically significant correlations.
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spelling pubmed-72981732020-06-17 Movies Emotional Analysis Using Textual Contents Kayhani, Amir Kazem Meziane, Farid Chiky, Raja Natural Language Processing and Information Systems Article In this paper, we use movies and series subtitles and applied text mining and Natural Language Processing methods to evaluate emotions in videos. Three different word lexicons were used and one of the outcomes of this research is the generation of a secondary dataset with more than 3658 records which can be used for other data analysis and data mining research. We used our secondary dataset to find and display correlations between different emotions on the videos and the correlation between emotions on the movies and users’ scores on IMDb using the Pearson correlation method and found some statistically significant correlations. 2020-05-26 /pmc/articles/PMC7298173/ http://dx.doi.org/10.1007/978-3-030-51310-8_19 Text en © Springer Nature Switzerland AG 2020 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 Article
Kayhani, Amir Kazem
Meziane, Farid
Chiky, Raja
Movies Emotional Analysis Using Textual Contents
title Movies Emotional Analysis Using Textual Contents
title_full Movies Emotional Analysis Using Textual Contents
title_fullStr Movies Emotional Analysis Using Textual Contents
title_full_unstemmed Movies Emotional Analysis Using Textual Contents
title_short Movies Emotional Analysis Using Textual Contents
title_sort movies emotional analysis using textual contents
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7298173/
http://dx.doi.org/10.1007/978-3-030-51310-8_19
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AT mezianefarid moviesemotionalanalysisusingtextualcontents
AT chikyraja moviesemotionalanalysisusingtextualcontents

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