Sentiment Analysis using various Machine Learning and Deep Learning Techniques

Sentiment analysis has gained a lot of attention from researchers in the last year because it has been widely applied to a variety of application domains such as business, government, education, sports, tourism, biomedicine, and telecommunication services. Sentiment analysis is an automated computa...

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Autores principales:	V Umarani, A Julian, J Deepa
Formato:	article
Lenguaje:	EN
Publicado:	Nigerian Society of Physical Sciences 2021
Materias:	Physics QC1-999
Acceso en línea:	https://doaj.org/article/ec6f11548c8f452095de66127da9c416
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spelling	oai:doaj.org-article:ec6f11548c8f452095de66127da9c4162021-11-30T12:19:08ZSentiment Analysis using various Machine Learning and Deep Learning Techniques10.46481/jnsps.2021.3082714-28172714-4704https://doaj.org/article/ec6f11548c8f452095de66127da9c4162021-11-01T00:00:00Zhttps://journal.nsps.org.ng/index.php/jnsps/article/view/308https://doaj.org/toc/2714-2817https://doaj.org/toc/2714-4704 Sentiment analysis has gained a lot of attention from researchers in the last year because it has been widely applied to a variety of application domains such as business, government, education, sports, tourism, biomedicine, and telecommunication services. Sentiment analysis is an automated computational method for studying or evaluating sentiments, feelings, and emotions expressed as comments, feedbacks, or critiques. The sentiment analysis process can be automated using machine learning techniques, which analyses text patterns faster. The supervised machine learning technique is the most used mechanism for sentiment analysis. The proposed work discusses the flow of sentiment analysis process and investigates the common supervised machine learning techniques such as multinomial naive bayes, Bernoulli naive bayes, logistic regression, support vector machine, random forest, K-nearest neighbor, decision tree, and deep learning techniques such as Long Short-Term Memory and Convolution Neural Network. The work examines such learning methods using standard data set and the experimental results of sentiment analysis demonstrate the performance of various classifiers taken in terms of the precision, recall, F1-score, RoC-Curve, accuracy, running time and k fold cross validation and helps in appreciating the novelty of the several deep learning techniques and also giving the user an overview of choosing the right technique for their application. V UmaraniA JulianJ DeepaNigerian Society of Physical SciencesarticlePhysicsQC1-999ENJournal of Nigerian Society of Physical Sciences, Vol 3, Iss 4 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Physics QC1-999
spellingShingle	Physics QC1-999 V Umarani A Julian J Deepa Sentiment Analysis using various Machine Learning and Deep Learning Techniques
description	Sentiment analysis has gained a lot of attention from researchers in the last year because it has been widely applied to a variety of application domains such as business, government, education, sports, tourism, biomedicine, and telecommunication services. Sentiment analysis is an automated computational method for studying or evaluating sentiments, feelings, and emotions expressed as comments, feedbacks, or critiques. The sentiment analysis process can be automated using machine learning techniques, which analyses text patterns faster. The supervised machine learning technique is the most used mechanism for sentiment analysis. The proposed work discusses the flow of sentiment analysis process and investigates the common supervised machine learning techniques such as multinomial naive bayes, Bernoulli naive bayes, logistic regression, support vector machine, random forest, K-nearest neighbor, decision tree, and deep learning techniques such as Long Short-Term Memory and Convolution Neural Network. The work examines such learning methods using standard data set and the experimental results of sentiment analysis demonstrate the performance of various classifiers taken in terms of the precision, recall, F1-score, RoC-Curve, accuracy, running time and k fold cross validation and helps in appreciating the novelty of the several deep learning techniques and also giving the user an overview of choosing the right technique for their application.
format	article
author	V Umarani A Julian J Deepa
author_facet	V Umarani A Julian J Deepa
author_sort	V Umarani
title	Sentiment Analysis using various Machine Learning and Deep Learning Techniques
title_short	Sentiment Analysis using various Machine Learning and Deep Learning Techniques
title_full	Sentiment Analysis using various Machine Learning and Deep Learning Techniques
title_fullStr	Sentiment Analysis using various Machine Learning and Deep Learning Techniques
title_full_unstemmed	Sentiment Analysis using various Machine Learning and Deep Learning Techniques
title_sort	sentiment analysis using various machine learning and deep learning techniques
publisher	Nigerian Society of Physical Sciences
publishDate	2021
url	https://doaj.org/article/ec6f11548c8f452095de66127da9c416
work_keys_str_mv	AT vumarani sentimentanalysisusingvariousmachinelearninganddeeplearningtechniques AT ajulian sentimentanalysisusingvariousmachinelearninganddeeplearningtechniques AT jdeepa sentimentanalysisusingvariousmachinelearninganddeeplearningtechniques
_version_	1718406659390308352

Sentiment Analysis using various Machine Learning and Deep Learning Techniques

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