A big data approach to sentiment analysis using greedy feature selection with cat swarm optimization-based long short-term memory neural networks

Sentiment analysis is crucial in various systems such as opinion mining and predicting. Considerable research has been done to analyze sentiment using various machine learning techniques. However, the high error rates in these studies can reduce the entire system’s efficiency. We introduce a novel b...

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Vydané v:	The Journal of supercomputing Ročník 76; číslo 6; s. 4414 - 4429
Hlavní autori:	Alarifi, Abdulaziz, Tolba, Amr, Al-Makhadmeh, Zafer, Said, Wael
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	New York Springer US 01.06.2020 Springer Nature B.V
Predmet:	Big Data Classifiers Compilers Computer Science Data mining Error analysis Greedy algorithms Interpreters Machine learning Neural networks Optimization Particle swarm optimization Processor Architectures Programming Languages Sentiment analysis System effectiveness Greedy algorithm Sentiment analysis Big data Cat swarm optimization-based long short-term memory neural network Particle swarm optimization Error rate precision
ISSN:	0920-8542, 1573-0484
On-line prístup:	Získať plný text
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