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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Vydáno v:The Journal of supercomputing Ročník 76; číslo 6; s. 4414 - 4429
Hlavní autoři: Alarifi, Abdulaziz, Tolba, Amr, Al-Makhadmeh, Zafer, Said, Wael
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York Springer US 01.06.2020
Springer Nature B.V
Témata:
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
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Shrnutí: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 big data and machine learning technique for evaluating sentiment analysis processes to overcome this problem. The data are collected from a huge volume of datasets, helpful in the effective analysis of systems. The noise in the data is eliminated using a preprocessing data mining concept. From the cleaned sentiment data, effective features are selected using a greedy approach that selects optimal features processed by an optimal classifier called cat swarm optimization-based long short-term memory neural network (CSO-LSTMNN). The classifiers analyze sentiment-related features according to cat behavior, minimizing error rate while examining features. This technique helps improve system efficiency, analyzed using experimental results of error rate, precision, recall, and accuracy. The results obtained by implementing the greedy feature and CSO-LSTMNN algorithm and the particle swarm optimization (PSO) algorithm are compared; CSO-LSTMNN outperforms PSO in terms of increasing accuracy and decreasing error rate.
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ISSN:0920-8542
1573-0484
DOI:10.1007/s11227-018-2398-2