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: | , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
New York
Springer US
01.06.2020
Springer Nature B.V |
| Témata: | |
| ISSN: | 0920-8542, 1573-0484 |
| On-line přístup: | Získat plný 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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| Cites_doi | 10.1016/j.asoc.2017.11.019 10.1007/978-3-319-60435-0_20 10.1007/s10957-013-0458-6 10.1016/j.knosys.2018.04.006 10.1016/j.patrec.2016.01.016 10.1016/j.asoc.2011.09.017 10.1007/978-1-4899-7687-1_907 10.1109/TKDE.2013.111 10.1007/s10115-013-0706-y 10.1007/978-981-10-3874-7_66 10.1007/s10586-018-2014-5 10.1016/j.patcog.2011.11.005 10.5815/ijmecs.2014.12.08 10.1109/TCBB.2015.2462349 10.1109/TKDE.2013.182 10.1016/j.knosys.2015.01.010 10.1109/72.963769.ISSN1045-9227 10.1007/s10922-017-9419-y 10.1007/978-981-10-3223-3_36 10.1007/s10462-016-9517-3 10.1561/1500000011 10.1080/24751839.2018.1423792 10.17485/ijst/2016/v9i32/100209 10.1007/s10994-013-5375-2 10.1108/K-08-2016-0196 |
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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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| References | Zhang, Liu (CR1) 2017 Pang, Lee (CR3) 2008; 2 Bhalla, Jain (CR12) 2016; 9 Yang, Sadat Hosseini, Gandomi (CR33) 2012; 12 Astorino, Fuduli (CR17) 2015; 164 Zhang, Zhao, Chow (CR18) 2015; 27 Hazewinkel (CR31) 2001 CR16 Roy, Biswas, Chaudhuri (CR8) 2014; 12 Subramanya, Bilmes (CR19) 2011; 12 Chakraborty, Kawamura (CR28) 2018; 2 Mahi, Baykan, Kodaz (CR9) 2018; 62 Bhatia, Sharma, Bhatia (CR4) 2018 Tolba, Elashkar (CR5) 2018; 22 Cecotti (CR20) 2016; 73 Nayak, Naik, Behera (CR27) 2016; 19 Black (CR30) 2005 Gers, Schmidhuber (CR32) 2001; 12 Meysam, Yasin, Mohammad, Mohammad (CR35) 2013; 11 Liu, Gao, Zhang, Lu, Chen, Liang, Tao (CR6) 2017; 14 Long, Wang, Ding, Pan, Yu (CR24) 2014; 26 Kumar, Mishra (CR34) 2013; 4 Perlich, Dalessandro, Raeder, Stitelman, Provost (CR23) 2015; 95 Nikitidis, Nikolaidis, Pitas (CR13) 2012; 45 Isaac, García, Herrera (CR15) 2015; 42 Lee, Jeong, Seo, Kim, Kang (CR2) 2018; 152 CR25 Nabaei, Hamian, Parsaei, Safdari, Samad-Soltani, Zarrabi, Ghassemi (CR7) 2018; 49 CR21 Lu, Behbood, Hao, Zuo, Xue, Zhang (CR22) 2015; 80 Pandey, Rajput, Mishra (CR10) 2018 Gill, Buyya, Chana, Singh, Abraham (CR11) 2018; 26 La, Cao, Qin (CR29) 2018; 47 Kingma, Mohamed, Rezende, Welling (CR14) 2014; 2 Kumar, Khorwal (CR26) 2017 A Kumar (2398_CR26) 2017 O Meysam (2398_CR35) 2013; 11 DP Kingma (2398_CR14) 2014; 2 SS Gill (2398_CR11) 2018; 26 FA Gers (2398_CR32) 2001; 12 2398_CR21 B Chakraborty (2398_CR28) 2018; 2 L La (2398_CR29) 2018; 47 H Cecotti (2398_CR20) 2016; 73 2398_CR25 S Bhatia (2398_CR4) 2018 HM Pandey (2398_CR10) 2018 A Subramanya (2398_CR19) 2011; 12 S Roy (2398_CR8) 2014; 12 J Lu (2398_CR22) 2015; 80 J Nayak (2398_CR27) 2016; 19 A Nabaei (2398_CR7) 2018; 49 T Isaac (2398_CR15) 2015; 42 (2398_CR31) 2001 S Nikitidis (2398_CR13) 2012; 45 R Bhalla (2398_CR12) 2016; 9 Amr Tolba (2398_CR5) 2018; 22 X-S Yang (2398_CR33) 2012; 12 A Kumar (2398_CR34) 2013; 4 M Mahi (2398_CR9) 2018; 62 Z Zhang (2398_CR18) 2015; 27 B Pang (2398_CR3) 2008; 2 Y Liu (2398_CR6) 2017; 14 C Perlich (2398_CR23) 2015; 95 M Long (2398_CR24) 2014; 26 PE Black (2398_CR30) 2005 Lei Zhang (2398_CR1) 2017 2398_CR16 Gichang Lee (2398_CR2) 2018; 152 A Astorino (2398_CR17) 2015; 164 |
| References_xml | – volume: 14 start-page: 108 year: 2017 end-page: 120 ident: CR6 article-title: Solving NP-hard problems with Physarum-based ant colony system publication-title: IEEE/ACM Trans Comput Biol Bioinf – volume: 95 start-page: 103 year: 2015 end-page: 127 ident: CR23 article-title: Machine learning for targeted display advertising: transfer learning in action publication-title: Mach Learn – start-page: 693 year: 2017 end-page: 703 ident: CR26 article-title: Firefly algorithm for feature selection in sentiment analysis publication-title: Computational intelligence in data mining – year: 2005 ident: CR30 publication-title: Greedy algorithm – volume: 27 start-page: 2362 year: 2015 end-page: 2376 ident: CR18 article-title: Graph based constrained semi-supervised learning framework via label propagation over adaptive neighborhood publication-title: IEEE Trans Knowl Data Eng – ident: CR16 – volume: 22 start-page: 3183 issue: S2 year: 2018 end-page: 3189 ident: CR5 article-title: Soft computing approaches based bookmark selection and clustering techniques for social tagging systems publication-title: Cluster Computing – volume: 49 start-page: 79 year: 2018 end-page: 103 ident: CR7 article-title: Topologies and performance of intelligent algorithms: a comprehensive review publication-title: Artif Intell Rev – start-page: 377 year: 2018 end-page: 384 ident: CR10 article-title: Performance comparison of pattern search, simulated annealing, genetic algorithm and jaya algorithm publication-title: Data engineering and intelligent computing – volume: 2 start-page: 1 year: 2018 end-page: 18 ident: CR28 article-title: A new penalty-based wrapper fitness function for feature subset selection with evolutionary algorithms publication-title: J Inf Telecommun – volume: 164 start-page: 1039 year: 2015 end-page: 1050 ident: CR17 article-title: Support vector machine polyhedral separability in semi supervised learning publication-title: J Optim Theory Appl – volume: 45 start-page: 1838 year: 2012 end-page: 1852 ident: CR13 article-title: Multiplicative update rules for incremental training of multiclass support vector machines publication-title: Pattern Recognit – volume: 12 start-page: 1333 year: 2001 end-page: 1340 ident: CR32 article-title: LSTM recurrent networks learn simple context-free and context-sensitive languages publication-title: IEEE Trans Neural Netw – volume: 80 start-page: 14 year: 2015 end-page: 23 ident: CR22 article-title: Transfer learning using computational intelligence: a survey publication-title: Knowl-Based Sys – ident: CR25 – volume: 47 start-page: 474 year: 2018 end-page: 486 ident: CR29 article-title: Take full advantage of unlabeled data for sentiment classification publication-title: Kybernetes – volume: 11 start-page: 32 year: 2013 end-page: 41 ident: CR35 article-title: A novel cat swarm optimization algorithm for unconstrained optimization problems publication-title: Int J Inf Technol Comp Sci – ident: CR21 – volume: 19 start-page: 197 year: 2016 end-page: 211 ident: CR27 article-title: A novel nature inspired firefly algorithm with higher order neural network: performance analysis publication-title: Eng Sci Technol – start-page: 503 year: 2018 end-page: 523 ident: CR4 article-title: Sentiment analysis and mining of opinions publication-title: Internet of things and big data analytics toward next-generation intelligence – volume: 62 start-page: 571 year: 2018 end-page: 578 ident: CR9 article-title: A new approach based on particle swarm optimization algorithm for solving data allocation problem publication-title: Appl Soft Comput – volume: 9 start-page: 32 year: 2016 ident: CR12 article-title: A model based on effective and intelligent sentiment mining: a review publication-title: Indian J Sci Technol – year: 2001 ident: CR31 publication-title: [1994] Greedy algorithm. Encyclopedia of mathematics – volume: 26 start-page: 361 year: 2018 end-page: 400 ident: CR11 article-title: BULLET: particle swarm optimization based scheduling technique for provisioned cloud resources publication-title: J Netw Sys Manag – volume: 12 start-page: 3311 year: 2011 end-page: 3370 ident: CR19 article-title: Semi-supervised learning with measure propagation publication-title: J Mach Learn Res – volume: 26 start-page: 1076 year: 2014 end-page: 1089 ident: CR24 article-title: Adaptation regularization: a general framework for transfer learning publication-title: IEEE Trans Knowl Data Eng – volume: 2 start-page: 1 year: 2008 end-page: 135 ident: CR3 article-title: Opinion mining and sentiment analysis publication-title: Found Trends Inf Retr – volume: 2 start-page: 3581 year: 2014 end-page: 3589 ident: CR14 article-title: Semi-supervised learning with deep generative models publication-title: Adv Neural Inf Proc Sys – start-page: 1152 year: 2017 end-page: 1161 ident: CR1 article-title: Sentiment Analysis and Opinion Mining publication-title: Encyclopedia of Machine Learning and Data Mining – volume: 73 start-page: 76 year: 2016 end-page: 82 ident: CR20 article-title: Active graph based semi-supervised learning using image matching: application to handwritten digit recognition publication-title: Pattern Recognit Lett. – volume: 152 start-page: 70 year: 2018 end-page: 82 ident: CR2 article-title: Sentiment classification with word localization based on weakly supervised learning with a convolutional neural network publication-title: Knowledge-Based Systems – volume: 12 start-page: 55 year: 2014 end-page: 65 ident: CR8 article-title: Nature-inspired swarm intelligence and its applications publication-title: Int J Mod Educ Comp Sci – volume: 12 start-page: 1180 year: 2012 end-page: 1186 ident: CR33 article-title: Firefly algorithm for solving non-convex economic dispatch problems with valve loading effect publication-title: Appl Soft Comput – volume: 4 start-page: 1185 year: 2013 ident: CR34 article-title: Cat swarm based optimization of gene expression data classification publication-title: Int J Comp Trends Technol (IJCTT) – volume: 42 start-page: 245 year: 2015 end-page: 284 ident: CR15 article-title: Self-labeled techniques for semi-supervised learning: taxonomy, software and empirical study publication-title: Knowl Inf Sys – volume: 62 start-page: 571 year: 2018 ident: 2398_CR9 publication-title: Appl Soft Comput doi: 10.1016/j.asoc.2017.11.019 – volume: 11 start-page: 32 year: 2013 ident: 2398_CR35 publication-title: Int J Inf Technol Comp Sci – start-page: 503 volume-title: Internet of things and big data analytics toward next-generation intelligence year: 2018 ident: 2398_CR4 doi: 10.1007/978-3-319-60435-0_20 – volume: 164 start-page: 1039 year: 2015 ident: 2398_CR17 publication-title: J Optim Theory Appl doi: 10.1007/s10957-013-0458-6 – ident: 2398_CR21 – volume-title: [1994] Greedy algorithm. Encyclopedia of mathematics year: 2001 ident: 2398_CR31 – volume: 152 start-page: 70 year: 2018 ident: 2398_CR2 publication-title: Knowledge-Based Systems doi: 10.1016/j.knosys.2018.04.006 – volume: 73 start-page: 76 year: 2016 ident: 2398_CR20 publication-title: Pattern Recognit Lett. doi: 10.1016/j.patrec.2016.01.016 – volume: 12 start-page: 1180 year: 2012 ident: 2398_CR33 publication-title: Appl Soft Comput doi: 10.1016/j.asoc.2011.09.017 – start-page: 1152 volume-title: Encyclopedia of Machine Learning and Data Mining year: 2017 ident: 2398_CR1 doi: 10.1007/978-1-4899-7687-1_907 – volume: 2 start-page: 3581 year: 2014 ident: 2398_CR14 publication-title: Adv Neural Inf Proc Sys – volume: 26 start-page: 1076 year: 2014 ident: 2398_CR24 publication-title: IEEE Trans Knowl Data Eng doi: 10.1109/TKDE.2013.111 – volume: 42 start-page: 245 year: 2015 ident: 2398_CR15 publication-title: Knowl Inf Sys doi: 10.1007/s10115-013-0706-y – start-page: 693 volume-title: Computational intelligence in data mining year: 2017 ident: 2398_CR26 doi: 10.1007/978-981-10-3874-7_66 – ident: 2398_CR25 – volume-title: Greedy algorithm year: 2005 ident: 2398_CR30 – volume: 22 start-page: 3183 issue: S2 year: 2018 ident: 2398_CR5 publication-title: Cluster Computing doi: 10.1007/s10586-018-2014-5 – volume: 45 start-page: 1838 year: 2012 ident: 2398_CR13 publication-title: Pattern Recognit doi: 10.1016/j.patcog.2011.11.005 – volume: 19 start-page: 197 year: 2016 ident: 2398_CR27 publication-title: Eng Sci Technol – volume: 12 start-page: 55 year: 2014 ident: 2398_CR8 publication-title: Int J Mod Educ Comp Sci doi: 10.5815/ijmecs.2014.12.08 – volume: 14 start-page: 108 year: 2017 ident: 2398_CR6 publication-title: IEEE/ACM Trans Comput Biol Bioinf doi: 10.1109/TCBB.2015.2462349 – ident: 2398_CR16 – volume: 27 start-page: 2362 year: 2015 ident: 2398_CR18 publication-title: IEEE Trans Knowl Data Eng doi: 10.1109/TKDE.2013.182 – volume: 80 start-page: 14 year: 2015 ident: 2398_CR22 publication-title: Knowl-Based Sys doi: 10.1016/j.knosys.2015.01.010 – volume: 12 start-page: 3311 year: 2011 ident: 2398_CR19 publication-title: J Mach Learn Res – volume: 12 start-page: 1333 year: 2001 ident: 2398_CR32 publication-title: IEEE Trans Neural Netw doi: 10.1109/72.963769.ISSN1045-9227 – volume: 26 start-page: 361 year: 2018 ident: 2398_CR11 publication-title: J Netw Sys Manag doi: 10.1007/s10922-017-9419-y – start-page: 377 volume-title: Data engineering and intelligent computing year: 2018 ident: 2398_CR10 doi: 10.1007/978-981-10-3223-3_36 – volume: 4 start-page: 1185 year: 2013 ident: 2398_CR34 publication-title: Int J Comp Trends Technol (IJCTT) – volume: 49 start-page: 79 year: 2018 ident: 2398_CR7 publication-title: Artif Intell Rev doi: 10.1007/s10462-016-9517-3 – volume: 2 start-page: 1 year: 2008 ident: 2398_CR3 publication-title: Found Trends Inf Retr doi: 10.1561/1500000011 – volume: 2 start-page: 1 year: 2018 ident: 2398_CR28 publication-title: J Inf Telecommun doi: 10.1080/24751839.2018.1423792 – volume: 9 start-page: 32 year: 2016 ident: 2398_CR12 publication-title: Indian J Sci Technol doi: 10.17485/ijst/2016/v9i32/100209 – volume: 95 start-page: 103 year: 2015 ident: 2398_CR23 publication-title: Mach Learn doi: 10.1007/s10994-013-5375-2 – volume: 47 start-page: 474 year: 2018 ident: 2398_CR29 publication-title: Kybernetes doi: 10.1108/K-08-2016-0196 |
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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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