Emotion AI-Driven Sentiment Analysis: A Survey, Future Research Directions, and Open Issues

The essential use of natural language processing is to analyze the sentiment of the author via the context. This sentiment analysis (SA) is said to determine the exactness of the underlying emotion in the context. It has been used in several subject areas such as stock market prediction, social medi...

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Detalles Bibliográficos
Autores: Chakriswaran, Priya, Vincent, Durai Raj, Srinivasan, Kathiravan, Sharma, Vishal, Chang, Chuan-Yu, Gutiérrez Reina, Daniel
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2019
País:España
Institución:Universidad de Sevilla (US)
Repositorio:idUS. Depósito de Investigación de la Universidad de Sevilla
OAI Identifier:oai:idus.us.es:11441/100005
Acceso en línea:https://hdl.handle.net/11441/100005
https://doi.org/10.3390/app9245462
Access Level:acceso abierto
Palabra clave:Emotion AI
Sentiment analysis
Multi-lingual sentiment analysis
Ontology
Machinelearning
Lexicon
Neural networks
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spelling Emotion AI-Driven Sentiment Analysis: A Survey, Future Research Directions, and Open IssuesChakriswaran, PriyaVincent, Durai RajSrinivasan, KathiravanSharma, VishalChang, Chuan-YuGutiérrez Reina, DanielEmotion AISentiment analysisMulti-lingual sentiment analysisOntologyMachinelearningLexiconNeural networksThe essential use of natural language processing is to analyze the sentiment of the author via the context. This sentiment analysis (SA) is said to determine the exactness of the underlying emotion in the context. It has been used in several subject areas such as stock market prediction, social media data on product reviews, psychology, judiciary, forecasting, disease prediction, agriculture, etc. Many researchers have worked on these areas and have produced significant results. These outcomes are beneficial in their respective fields, as they help to understand the overall summary in a short time. Furthermore, SA helps in understanding actual feedback shared across di erent platforms such as Amazon, TripAdvisor, etc. The main objective of this thorough survey was to analyze some of the essential studies done so far and to provide an overview of SA models in the area of emotion AI-driven SA. In addition, this paper o ers a review of ontology-based SA and lexicon-based SA along with machine learning models that are used to analyze the sentiment of the given context. Furthermore, this work also discusses di erent neural network-based approaches for analyzing sentiment. Finally, these di erent approaches were also analyzed with sample data collected from Twitter. Among the four approaches considered in each domain, the aspect-based ontology method produced 83% accuracy among the ontology-based SAs, the term frequency approach produced 85% accuracy in the lexicon-based analysis, and the support vector machine-based approach achieved 90% accuracy among the other machine learning-based approaches.Ministerio de Educación (MOE) en Taiwán N/AMDPIIngeniería ElectrónicaMinistry of Education (MOE) in Taiwan2019info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfapplication/pdfhttps://hdl.handle.net/11441/100005https://doi.org/10.3390/app9245462reponame:idUS. Depósito de Investigación de la Universidad de Sevillainstname:Universidad de Sevilla (US)InglésApplied Sciences, 9 (24), Article number 5462.N/Ahttps://doi.org/10.3390/app9245462info:eu-repo/semantics/openAccessoai:idus.us.es:11441/1000052026-06-17T12:51:07Z
dc.title.none.fl_str_mv Emotion AI-Driven Sentiment Analysis: A Survey, Future Research Directions, and Open Issues
title Emotion AI-Driven Sentiment Analysis: A Survey, Future Research Directions, and Open Issues
spellingShingle Emotion AI-Driven Sentiment Analysis: A Survey, Future Research Directions, and Open Issues
Chakriswaran, Priya
Emotion AI
Sentiment analysis
Multi-lingual sentiment analysis
Ontology
Machinelearning
Lexicon
Neural networks
title_short Emotion AI-Driven Sentiment Analysis: A Survey, Future Research Directions, and Open Issues
title_full Emotion AI-Driven Sentiment Analysis: A Survey, Future Research Directions, and Open Issues
title_fullStr Emotion AI-Driven Sentiment Analysis: A Survey, Future Research Directions, and Open Issues
title_full_unstemmed Emotion AI-Driven Sentiment Analysis: A Survey, Future Research Directions, and Open Issues
title_sort Emotion AI-Driven Sentiment Analysis: A Survey, Future Research Directions, and Open Issues
dc.creator.none.fl_str_mv Chakriswaran, Priya
Vincent, Durai Raj
Srinivasan, Kathiravan
Sharma, Vishal
Chang, Chuan-Yu
Gutiérrez Reina, Daniel
author Chakriswaran, Priya
author_facet Chakriswaran, Priya
Vincent, Durai Raj
Srinivasan, Kathiravan
Sharma, Vishal
Chang, Chuan-Yu
Gutiérrez Reina, Daniel
author_role author
author2 Vincent, Durai Raj
Srinivasan, Kathiravan
Sharma, Vishal
Chang, Chuan-Yu
Gutiérrez Reina, Daniel
author2_role author
author
author
author
author
dc.contributor.none.fl_str_mv Ingeniería Electrónica
Ministry of Education (MOE) in Taiwan
dc.subject.none.fl_str_mv Emotion AI
Sentiment analysis
Multi-lingual sentiment analysis
Ontology
Machinelearning
Lexicon
Neural networks
topic Emotion AI
Sentiment analysis
Multi-lingual sentiment analysis
Ontology
Machinelearning
Lexicon
Neural networks
description The essential use of natural language processing is to analyze the sentiment of the author via the context. This sentiment analysis (SA) is said to determine the exactness of the underlying emotion in the context. It has been used in several subject areas such as stock market prediction, social media data on product reviews, psychology, judiciary, forecasting, disease prediction, agriculture, etc. Many researchers have worked on these areas and have produced significant results. These outcomes are beneficial in their respective fields, as they help to understand the overall summary in a short time. Furthermore, SA helps in understanding actual feedback shared across di erent platforms such as Amazon, TripAdvisor, etc. The main objective of this thorough survey was to analyze some of the essential studies done so far and to provide an overview of SA models in the area of emotion AI-driven SA. In addition, this paper o ers a review of ontology-based SA and lexicon-based SA along with machine learning models that are used to analyze the sentiment of the given context. Furthermore, this work also discusses di erent neural network-based approaches for analyzing sentiment. Finally, these di erent approaches were also analyzed with sample data collected from Twitter. Among the four approaches considered in each domain, the aspect-based ontology method produced 83% accuracy among the ontology-based SAs, the term frequency approach produced 85% accuracy in the lexicon-based analysis, and the support vector machine-based approach achieved 90% accuracy among the other machine learning-based approaches.
publishDate 2019
dc.date.none.fl_str_mv 2019
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv https://hdl.handle.net/11441/100005
https://doi.org/10.3390/app9245462
url https://hdl.handle.net/11441/100005
https://doi.org/10.3390/app9245462
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv Applied Sciences, 9 (24), Article number 5462.
N/A
https://doi.org/10.3390/app9245462
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv MDPI
publisher.none.fl_str_mv MDPI
dc.source.none.fl_str_mv reponame:idUS. Depósito de Investigación de la Universidad de Sevilla
instname:Universidad de Sevilla (US)
instname_str Universidad de Sevilla (US)
reponame_str idUS. Depósito de Investigación de la Universidad de Sevilla
collection idUS. Depósito de Investigación de la Universidad de Sevilla
repository.name.fl_str_mv
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