A Centered Convolutional Restricted Boltzmann Machine Optimized by Hybrid Atom Search Arithmetic Optimization Algorithm for Sentimental Analysis
Sentiment analysis uses natural language processing (NLP) to track online conversations and uncover additional information about a subject, business, or theme. Existing machine-learning algorithms are accurate and perform well, but they struggle to reduce computational time and cope with the noisy a...
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| Vydáno v: | Neural processing letters Ročník 54; číslo 5; s. 4123 - 4151 |
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| Hlavní autoři: | , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
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Springer US
01.10.2022
Springer Nature B.V |
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| ISSN: | 1370-4621, 1573-773X |
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| Abstract | Sentiment analysis uses natural language processing (NLP) to track online conversations and uncover additional information about a subject, business, or theme. Existing machine-learning algorithms are accurate and perform well, but they struggle to reduce computational time and cope with the noisy and high-dimensional feature space of social media data. To resolve these concerns, this paper introduced a Centered Convolutional Restricted Boltzmann Machines (CCRBM), a revolutionary deep learning technique for user behavioral sentimental analysis. The DBN architecture is mainly selected in this work due to its ability to extract in-depth sentimental features, dimensionality reduction, and higher classification accuracy. However, the improper parameter setting can lead to non-convergence, large randomness, and weak generalization capability. To tackle this issue, this work proposes a Hybrid Atom Search Arithmetic Optimization (HASAO) approach, which optimizes DBN parameters such as batch size and decay rate while minimizing DBN issues such as randomness and instability. The performance of the proposed model is analyzed by comparing it with different baseline models and the accuracy value above 90% for the nine datasets proves the efficiency of the proposed technique. When compared to the existing techniques, the proposed methodology offers improved accuracy and speedup capacity. |
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| AbstractList | Sentiment analysis uses natural language processing (NLP) to track online conversations and uncover additional information about a subject, business, or theme. Existing machine-learning algorithms are accurate and perform well, but they struggle to reduce computational time and cope with the noisy and high-dimensional feature space of social media data. To resolve these concerns, this paper introduced a Centered Convolutional Restricted Boltzmann Machines (CCRBM), a revolutionary deep learning technique for user behavioral sentimental analysis. The DBN architecture is mainly selected in this work due to its ability to extract in-depth sentimental features, dimensionality reduction, and higher classification accuracy. However, the improper parameter setting can lead to non-convergence, large randomness, and weak generalization capability. To tackle this issue, this work proposes a Hybrid Atom Search Arithmetic Optimization (HASAO) approach, which optimizes DBN parameters such as batch size and decay rate while minimizing DBN issues such as randomness and instability. The performance of the proposed model is analyzed by comparing it with different baseline models and the accuracy value above 90% for the nine datasets proves the efficiency of the proposed technique. When compared to the existing techniques, the proposed methodology offers improved accuracy and speedup capacity. |
| Author | Sethukarasi, T. Karthik, E. |
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| Cites_doi | 10.7910/DVN/PDI7IN 10.1016/j.cma.2020.113609 10.1016/j.eswa.2019.112834 10.1016/j.techsoc.2016.10.003 10.1016/j.future.2020.10.027 10.1016/j.cogsys.2018.10.001 10.1007/s13042-020-01181-9 10.1109/TNN.2008.2000444 10.1016/j.bspc.2021.102480 10.1016/j.eswa.2018.03.055 10.1016/j.ipm.2020.102290 10.1609/icwsm.v14i1.7347 10.1007/s11227-021-04028-4 10.1109/ACCESS.2020.3028260 10.1016/j.jocs.2015.04.014 10.1109/ACCESS.2021.3053917 10.1016/j.knosys.2018.08.030 10.1109/IACC.2017.0186 10.1109/NGCT.2016.7877399 10.1016/j.knosys.2020.106091 10.4249/scholarpedia.5947 10.1016/j.ijhcs.2017.09.005 10.1016/j.comcom.2020.02.044 10.1016/j.neucom.2016.06.055 10.1016/j.future.2018.05.037 10.1016/j.patrec.2020.07.035 10.1016/j.knosys.2021.107332 |
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| Keywords | Sentiment analysis Hybrid optimization Centered convolutional restricted Boltzmann machines Social media User behavioral analysis |
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| PublicationTitle | Neural processing letters |
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Neurocomputing MukherjeeSBalaPKSarcasm detection in microblogs using Naïve Bayes and fuzzy clusteringTechnol Soc201748192710.1016/j.techsoc.2016.10.003 Es-SaberyFHairAQadirJSainz-De-AbajoBGarcía-ZapirainBDe La Torre-DíezISentence-level classification using parallel fuzzy deep learning classifierIEEE Access20219179431798510.1109/ACCESS.2021.3053917 AbdalgaderKAl ShibliAExperimental results on customer reviews using lexicon-based word polarity identification methodIEEE Access2020817995517996910.1109/ACCESS.2020.3028260 AlhalabiWJariJussilaKJVisviziAQureshiHMiltiadisLytrasAreejMalibariAdhamRSSocial mining for terroristic behavior detection through Arabic tweets characterizationFutur Gener Comput Syst202111613214410.1016/j.future.2020.10.027 AbualigahLDiabatAMirjaliliSElazizMAGandomiAHThe arithmetic optimization algorithmComput Methods Appl Mech Eng2021376113609419929910.1016/j.cma.2020.113609 Trupthi M, Pabboju S, Narasimha G (2017) Sentiment analysis on twitter using streaming API. In: 2017 IEEE 7th International Advance Computing Conference (IACC), pp 915–919. IEEE. Ji, Yu, Wen Wu, Shiyun Chen, Qin Chen, Wenxin Hu, and Liang He. (2020) Two-stage sentiment classification based on user-product interactive information. Knowledge-Based Systems 203: 106091. Salakhutdinov R, Hinton G (2009) Deep boltzmann machines. In: Artificial Intelligence and Statistics, pp. 448–455. PMLR GaoJYangJWangGLiMA novel feature extraction method for scene recognition based on centered convolutional restricted Boltzmann machinesNeurocomputing201621470871710.1016/j.neucom.2016.06.055 Baumgartner J, Savvas Z, Brian K, Megan S, Jeremy B (2020) The pushshift reddit dataset. In: Proceedings of the International AAAI Conference on Web and Social Media, vol 14, pp 830–839. SundararajVAn efficient threshold prediction scheme for wavelet based ECG signal noise reduction using variable step size firefly algorithmInt J Intell Eng Syst201693117126 GohC-KTeohE-JTanKCHybrid multiobjective evolutionary design for artificial neural networksIEEE Trans Neural Netw20081991531154810.1109/TNN.2008.2000444 Alharbi ASM, de Doncker E (2019) Twitter sentiment analysis with a deep neural network: An enhanced approach using user behavioral information. Cogn Syst Res 54: 50-61. ZhaoWWangLZhangZAtom search optimization and its application to solve a hydrogeologic parameter estimation problemKnowl-Based Syst201916328330410.1016/j.knosys.2018.08.030 Li, Da, RafalRzepka, Michal Ptaszynski, and Kenji Araki. (2020) HEMOS: A novel deep learning-based fine-grained humor detecting method for sentiment analysis of social media. Information Processing & Management 57(6): 102290. Perikos I, Spyridon K, Ioannis H (2021) Sentiment analysis using novel and interpretable architectures of Hidden Markov Models. Knowl Based Syst 107332. ChenLYanDWangFUser perception of sentiment-integrated critiquing in recommender systemsInt J Hum Comput Stud201912142010.1016/j.ijhcs.2017.09.005 Demographics of Social Media Users and Adoption in the United States (2021) Pew Research Center: Internet, Science & Tech, 05-Jun-2020. Retrieved February 13, 2021, from https://www.pewresearch.org/internet/fact-sheet/social-media Littman J, Wrubel L, Kerchner D (2016) 2016 United States Presidential Election Tweet Ids. https://doi.org/10.7910/DVN/PDI7IN, Harvard Dataverse, V3 VashishthaSSebaSFuzzy rule based unsupervised sentiment analysis from social media postsExpert Syst Appl201913811283410.1016/j.eswa.2019.112834 ZhaoJZengDXiaoYCheLWangMUser personality prediction based on topic preference and sentiment analysis using LSTM modelPattern Recogn Lett202013839740210.1016/j.patrec.2020.07.035 HintonGEDeep belief networksScholarpedia200945594710.4249/scholarpedia.5947 KarthikESethukarasiTSarcastic user behavior classification and prediction from social media data using firebug swarm optimization-based long short-term memoryJ Supercomput20217845333535710.1007/s11227-021-04028-4 YooSoYeopSongJeInJeongOkRanSocial media contents based sentiment analysis and prediction systemExpert Syst Appl201810510211110.1016/j.eswa.2018.03.055 N, L. (2019, March 09). IMDB Dataset of 50K Movie Reviews. Retrieved from https://www.kaggle.com/lakshmi25npathi/imdb-dataset-of-50k-movie-reviews AlamMAbidFGuangpeiCYunrongLVSocial media sentiment analysis through parallel dilated convolutional neural network for smart city applicationsComput Commun202015412913710.1016/j.comcom.2020.02.044 PapaJPRosaGHMaranaANScheirerWCoxDDModel selection for discriminative restricted Boltzmann machines through meta-heuristic techniquesJ Comput Sci20159141810.1016/j.jocs.2015.04.014 Rana S, Archana S (2016) Comparative analysis of sentiment orientation using SVM and Naive Bayes techniques. In: 2016 2nd International Conference on Next Generation Computing Technologies (NGCT), pp. 106–111. IEEE ZhaoWWangLZhangZA novel atom search optimization for dispersion coefficient estimation in groundwaterFutur Gener Comput Syst20199160161010.1016/j.future.2018.05.037 Yelp, I. (2020, March 26). Yelp Dataset. Retrieved from https://www.kaggle.com/yelp-dataset/yelp-dataset Feng, Shi, Kaisong Song, Daling Wang, Wei Gao, and Yifei Zhang. (2020) InterSentiment: combining deep neural models on interaction and sentiment for review rating prediction. International Journal of Machine Learning and Cybernetics pp: 1–12. Jose J, Gautam N, Tiwari M, Tiwari T, Suresh A, Sundararaj V, and ejeesh MR (2021) An image quality enhancement scheme employing adolescent identity search algorithm in the NSST domain for multimodal medical image fusion. In: Biomedical Signal Processing and Control, 66, p.102480. 10797_CR27 10797_CR5 L Chen (10797_CR14) 2019; 121 10797_CR28 W Zhao (10797_CR24) 2019; 163 10797_CR29 10797_CR3 JP Papa (10797_CR12) 2015; 9 10797_CR22 V Sundararaj (10797_CR35) 2016; 9 10797_CR1 M Alam (10797_CR4) 2020; 154 C-K Goh (10797_CR13) 2008; 19 10797_CR20 10797_CR21 E Karthik (10797_CR37) 2021; 78 L Abualigah (10797_CR26) 2021; 376 W Alhalabi (10797_CR19) 2021; 116 10797_CR15 W Zhao (10797_CR25) 2019; 91 10797_CR17 10797_CR33 J Zhao (10797_CR16) 2020; 138 F Es-Sabery (10797_CR34) 2021; 9 10797_CR36 10797_CR30 S Vashishtha (10797_CR6) 2019; 138 10797_CR31 K Abdalgader (10797_CR7) 2020; 8 L Zhang (10797_CR9) 2018; 8 10797_CR32 L Abualigah (10797_CR2) 2021; 376 S Mukherjee (10797_CR18) 2017; 48 SoYeop Yoo (10797_CR8) 2018; 105 J Gao (10797_CR10) 2016; 214 GE Hinton (10797_CR11) 2009; 4 S Baccianella (10797_CR23) 2010; 10 |
| References_xml | – reference: HintonGEDeep belief networksScholarpedia200945594710.4249/scholarpedia.5947 – reference: Perikos I, Spyridon K, Ioannis H (2021) Sentiment analysis using novel and interpretable architectures of Hidden Markov Models. Knowl Based Syst 107332. – reference: Yelp, I. (2020, March 26). Yelp Dataset. Retrieved from https://www.kaggle.com/yelp-dataset/yelp-dataset – reference: PapaJPRosaGHMaranaANScheirerWCoxDDModel selection for discriminative restricted Boltzmann machines through meta-heuristic techniquesJ Comput Sci20159141810.1016/j.jocs.2015.04.014 – reference: KarthikESethukarasiTSarcastic user behavior classification and prediction from social media data using firebug swarm optimization-based long short-term memoryJ Supercomput20217845333535710.1007/s11227-021-04028-4 – reference: MukherjeeSBalaPKSarcasm detection in microblogs using Naïve Bayes and fuzzy clusteringTechnol Soc201748192710.1016/j.techsoc.2016.10.003 – reference: Jose J, Gautam N, Tiwari M, Tiwari T, Suresh A, Sundararaj V, and ejeesh MR (2021) An image quality enhancement scheme employing adolescent identity search algorithm in the NSST domain for multimodal medical image fusion. In: Biomedical Signal Processing and Control, 66, p.102480. – reference: Rana S, Archana S (2016) Comparative analysis of sentiment orientation using SVM and Naive Bayes techniques. In: 2016 2nd International Conference on Next Generation Computing Technologies (NGCT), pp. 106–111. IEEE – reference: ZhangLWangSLiuBDeep learning for sentiment analysis: a surveyWiley Interdisc Rev201884e1253 – reference: You Z, Wang J, Zhang X (2021) Conciseness is better: recurrent attention LSTM model for document-level sentiment analysis. Neurocomputing – reference: ChenLYanDWangFUser perception of sentiment-integrated critiquing in recommender systemsInt J Hum Comput Stud201912142010.1016/j.ijhcs.2017.09.005 – reference: ZhaoJZengDXiaoYCheLWangMUser personality prediction based on topic preference and sentiment analysis using LSTM modelPattern Recogn Lett202013839740210.1016/j.patrec.2020.07.035 – reference: Li, Da, RafalRzepka, Michal Ptaszynski, and Kenji Araki. (2020) HEMOS: A novel deep learning-based fine-grained humor detecting method for sentiment analysis of social media. Information Processing & Management 57(6): 102290. – reference: BaccianellaSAndreaEFabrizioSSentiwordnet 3.0: an enhanced lexical resource for sentiment analysis and opinion miningLrec201010201022002204 – reference: Baumgartner J, Savvas Z, Brian K, Megan S, Jeremy B (2020) The pushshift reddit dataset. In: Proceedings of the International AAAI Conference on Web and Social Media, vol 14, pp 830–839. – reference: AlamMAbidFGuangpeiCYunrongLVSocial media sentiment analysis through parallel dilated convolutional neural network for smart city applicationsComput Commun202015412913710.1016/j.comcom.2020.02.044 – reference: YooSoYeopSongJeInJeongOkRanSocial media contents based sentiment analysis and prediction systemExpert Syst Appl201810510211110.1016/j.eswa.2018.03.055 – reference: AbualigahLDiabatAMirjaliliSElazizMAGandomiAHThe arithmetic optimization algorithmComput Methods Appl Mech Eng2021376113609419929910.1016/j.cma.2020.113609 – reference: AbdalgaderKAl ShibliAExperimental results on customer reviews using lexicon-based word polarity identification methodIEEE Access2020817995517996910.1109/ACCESS.2020.3028260 – reference: Feng, Shi, Kaisong Song, Daling Wang, Wei Gao, and Yifei Zhang. (2020) InterSentiment: combining deep neural models on interaction and sentiment for review rating prediction. International Journal of Machine Learning and Cybernetics pp: 1–12. – reference: Trupthi M, Pabboju S, Narasimha G (2017) Sentiment analysis on twitter using streaming API. In: 2017 IEEE 7th International Advance Computing Conference (IACC), pp 915–919. IEEE. – reference: Demographics of Social Media Users and Adoption in the United States (2021) Pew Research Center: Internet, Science & Tech, 05-Jun-2020. Retrieved February 13, 2021, from https://www.pewresearch.org/internet/fact-sheet/social-media/ – reference: N, L. (2019, March 09). IMDB Dataset of 50K Movie Reviews. Retrieved from https://www.kaggle.com/lakshmi25npathi/imdb-dataset-of-50k-movie-reviews – reference: GohC-KTeohE-JTanKCHybrid multiobjective evolutionary design for artificial neural networksIEEE Trans Neural Netw20081991531154810.1109/TNN.2008.2000444 – reference: ZhaoWWangLZhangZAtom search optimization and its application to solve a hydrogeologic parameter estimation problemKnowl-Based Syst201916328330410.1016/j.knosys.2018.08.030 – reference: Srinivasa-Desikan, Bhargav. Natural Language Processing and Computational Linguistics: A practical guide to text analysis with Python, Gensim, spaCy, and Keras. Packt Publishing Ltd, 2018. – reference: Alharbi ASM, de Doncker E (2019) Twitter sentiment analysis with a deep neural network: An enhanced approach using user behavioral information. 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| SubjectTerms | Accuracy Algorithms Arithmetic Artificial Intelligence Complex Systems Computational Intelligence Computer Science Computing time Data mining Datasets Decay rate Deep learning Machine learning Mathematical models Natural language processing Neural networks Optimization Parameters Randomness Sentiment analysis Social networks Stability analysis User behavior |
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| Title | A Centered Convolutional Restricted Boltzmann Machine Optimized by Hybrid Atom Search Arithmetic Optimization Algorithm for Sentimental Analysis |
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