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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| Published in: | The Journal of supercomputing Vol. 76; no. 6; pp. 4414 - 4429 |
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| Main Authors: | , , , |
| Format: | Journal Article |
| Language: | English |
| Published: |
New York
Springer US
01.06.2020
Springer Nature B.V |
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| ISSN: | 0920-8542, 1573-0484 |
| Online Access: | Get full text |
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| Abstract | 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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| AbstractList | 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. |
| Author | Al-Makhadmeh, Zafer Tolba, Amr Alarifi, Abdulaziz Said, Wael |
| Author_xml | – sequence: 1 givenname: Abdulaziz surname: Alarifi fullname: Alarifi, Abdulaziz organization: Computer Science Department, Community College, King Saud University – sequence: 2 givenname: Amr surname: Tolba fullname: Tolba, Amr email: atolba@ksu.edu.sa organization: Computer Science Department, Community College, King Saud University, Mathematics and Computer Science Department, Faculty of Science, Menoufia University – sequence: 3 givenname: Zafer surname: Al-Makhadmeh fullname: Al-Makhadmeh, Zafer organization: Computer Science Department, Community College, King Saud University – sequence: 4 givenname: Wael surname: Said fullname: Said, Wael organization: Computer Science Department, Faculty of Computers and Information, Zagazig University |
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| Copyright | Springer Science+Business Media, LLC, part of Springer Nature 2018 Springer Science+Business Media, LLC, part of Springer Nature 2018. |
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| Keywords | Greedy algorithm Sentiment analysis Big data Cat swarm optimization-based long short-term memory neural network Particle swarm optimization Error rate precision |
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| SubjectTerms | 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 |
| Title | A big data approach to sentiment analysis using greedy feature selection with cat swarm optimization-based long short-term memory neural networks |
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