Enhancing Sentiment Analysis via Random Majority Under-Sampling with Reduced Time Complexity for Classifying Tweet Reviews
Twitter has become a unique platform for social interaction from people all around the world, leading to an extensive amount of knowledge that can be used for various reasons. People share and spread their own ideologies and point of views on unique topics leading to the production of a lot of conte...
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| Published in | Electronics (Basel) Vol. 11; no. 21; p. 3624 |
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| Main Authors | , , , , |
| Format | Journal Article |
| Language | English |
| Published |
Basel
MDPI AG
01.11.2022
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| Subjects | |
| Online Access | Get full text |
| ISSN | 2079-9292 2079-9292 |
| DOI | 10.3390/electronics11213624 |
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| Abstract | Twitter has become a unique platform for social interaction from people all around the world, leading to an extensive amount of knowledge that can be used for various reasons. People share and spread their own ideologies and point of views on unique topics leading to the production of a lot of content. Sentiment analysis is of extreme importance to various businesses as it can directly impact their important decisions. Several challenges related to the research subject of sentiment analysis includes issues such as imbalanced dataset, lexical uniqueness, and processing time complexity. Most machine learning models are sequential: they need a considerable amount of time to complete execution. Therefore, we propose a model sentiment analysis specifically designed for imbalanced datasets that can reduce the time complexity of the task by using various text sequenced preprocessing techniques combined with random majority under-sampling. Our proposed model provides competitive results to other models while simultaneously reducing the time complexity for sentiment analysis. The results obtained after the experimentation corroborate that our model provides great results producing the accuracy of 86.5% and F1 score of 0.874 through XGB. |
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| AbstractList | Twitter has become a unique platform for social interaction from people all around the world, leading to an extensive amount of knowledge that can be used for various reasons. People share and spread their own ideologies and point of views on unique topics leading to the production of a lot of content. Sentiment analysis is of extreme importance to various businesses as it can directly impact their important decisions. Several challenges related to the research subject of sentiment analysis includes issues such as imbalanced dataset, lexical uniqueness, and processing time complexity. Most machine learning models are sequential: they need a considerable amount of time to complete execution. Therefore, we propose a model sentiment analysis specifically designed for imbalanced datasets that can reduce the time complexity of the task by using various text sequenced preprocessing techniques combined with random majority under-sampling. Our proposed model provides competitive results to other models while simultaneously reducing the time complexity for sentiment analysis. The results obtained after the experimentation corroborate that our model provides great results producing the accuracy of 86.5% and F1 score of 0.874 through XGB. |
| Audience | Academic |
| Author | Almuayqil, Saleh Naif Jhanjhi, N. Z. Almufareh, Maram Fahaad Khan, Navid Ali Humayun, Mamoona |
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| Cites_doi | 10.1109/eStream.2019.8732167 10.3390/app10155164 10.1016/j.knosys.2021.107134 10.1109/ICACSIS51025.2020.9263087 10.3390/app12083806 10.1016/j.patrec.2022.04.004 10.1145/3041021.3054223 10.1016/j.asoc.2016.11.022 10.1016/j.cosrev.2021.100413 10.1016/j.asoc.2020.106754 10.1007/978-981-16-3153-5_53 10.1109/IRI.2015.39 10.1007/s12530-018-9261-9 10.1109/ACCESS.2020.2969854 10.1007/s10462-019-09794-5 10.1016/j.eswa.2021.115019 10.1109/TCI.2020.3006727 10.32604/csse.2022.019288 10.1016/j.eswa.2017.03.042 10.1155/2022/7028717 10.1063/1.4994463 10.1007/978-3-642-37256-8_2 10.1109/ACCESS.2022.3149482 10.1162/COLI_a_00049 10.1561/1500000011 10.1016/j.eswa.2019.112834 10.1109/ICAwST.2019.8923260 10.3390/electronics11193058 10.1016/j.asej.2014.04.011 10.3390/mca23010011 10.1371/journal.pone.0245909 10.1109/ICAwST.2019.8923218 10.3390/info9040100 |
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| References | Pang (ref_5) 2008; 2 Zainuddin (ref_20) 2017; 48 Zhou (ref_30) 2020; 2020 Wang (ref_3) 2012; 31 ref_34 ref_11 Bahadir (ref_38) 2020; 6 ref_10 ref_31 Catal (ref_33) 2017; 50 ref_19 ref_39 Wassan (ref_13) 2021; 30 ref_15 ref_37 Humayun (ref_16) 2022; 40 Medhat (ref_1) 2014; 5 Yang (ref_17) 2020; 8 Birjali (ref_21) 2021; 226 ref_25 ref_24 ref_46 ref_45 ref_44 Jain (ref_7) 2021; 41 ref_43 Hussein (ref_22) 2018; 30 ref_42 ref_41 ref_40 Humayun (ref_23) 2022; 2022 Bibi (ref_36) 2022; 158 Rao (ref_29) 2019; 11 ref_2 Chakraborty (ref_18) 2020; 97 ref_28 Jing (ref_14) 2021; 178 ref_27 ref_26 ref_9 Vashishtha (ref_12) 2019; 138 Obiedat (ref_35) 2022; 10 Liu (ref_32) 2017; 80 Yadav (ref_8) 2019; 53 ref_4 ref_6 |
| References_xml | – volume: 30 start-page: 695 year: 2021 ident: ref_13 article-title: Amazon Product Sentiment Analysis using Machine Learning Techniques Amazon Product Sentiment Analysis using Machine Learning Techniques View project employing recent technologies for digital governance View project Amazon Product Sentiment Analysis using Machine Learning Techniques publication-title: Rev. Argent. – ident: ref_15 doi: 10.1109/eStream.2019.8732167 – ident: ref_39 doi: 10.3390/app10155164 – ident: ref_9 – volume: 48 start-page: 1218 year: 2017 ident: ref_20 article-title: Hybrid sentiment classification on twitter aspect-based sentiment analysis publication-title: Appl. Intell. – volume: 226 start-page: 107134 year: 2021 ident: ref_21 article-title: A comprehensive survey on sentiment analysis: Approaches, challenges and trends publication-title: Knowl.-Based Syst. doi: 10.1016/j.knosys.2021.107134 – ident: ref_45 doi: 10.1109/ICACSIS51025.2020.9263087 – ident: ref_2 doi: 10.3390/app12083806 – volume: 158 start-page: 80 year: 2022 ident: ref_36 article-title: A novel unsupervised ensemble framework using concept-based linguistic methods and machine learning for twitter sentiment analysis publication-title: Pattern Recognit. Lett. doi: 10.1016/j.patrec.2022.04.004 – ident: ref_42 doi: 10.1145/3041021.3054223 – volume: 50 start-page: 135 year: 2017 ident: ref_33 article-title: A sentiment classification model based on multiple classifiers publication-title: Appl. Soft Comput. doi: 10.1016/j.asoc.2016.11.022 – volume: 41 start-page: 10043 year: 2021 ident: ref_7 article-title: A systematic literature review on machine learning applications for consumer sentiment analysis using online reviews publication-title: Comput. Sci. Rev. doi: 10.1016/j.cosrev.2021.100413 – volume: 30 start-page: 330 year: 2018 ident: ref_22 article-title: A survey on sentiment analysis challenges publication-title: J. King Saud Univ.-Eng. Sci. – ident: ref_40 – volume: 97 start-page: 106754 year: 2020 ident: ref_18 article-title: Sentiment Analysis of COVID-19 tweets by Deep Learning Classifiers—A study to show how popularity is affecting accuracy in social media publication-title: Appl. Soft Comput. doi: 10.1016/j.asoc.2020.106754 – ident: ref_19 doi: 10.1007/978-981-16-3153-5_53 – ident: ref_26 doi: 10.1109/IRI.2015.39 – volume: 11 start-page: 119 year: 2019 ident: ref_29 article-title: A novel under sampling strategy for efficient software defect analysis of skewed distributed data publication-title: Evol. Syst. doi: 10.1007/s12530-018-9261-9 – volume: 31 start-page: 5 year: 2012 ident: ref_3 article-title: The Evolution of Social Commerce: The People, Management, Technology, and Information Dimensions publication-title: Commun. Assoc. Inf. Syst. – volume: 8 start-page: 23522 year: 2020 ident: ref_17 article-title: Sentiment Analysis for E-Commerce Product Reviews in Chinese Based on Sentiment Lexicon and Deep Learning publication-title: IEEE Access doi: 10.1109/ACCESS.2020.2969854 – volume: 53 start-page: 4335 year: 2019 ident: ref_8 article-title: Sentiment analysis using deep learning architectures: A review publication-title: Artif. Intell. Rev. doi: 10.1007/s10462-019-09794-5 – ident: ref_4 – volume: 178 start-page: 115019 year: 2021 ident: ref_14 article-title: A hybrid model integrating deep learning with investor sentiment analysis for stock price prediction publication-title: Expert Syst. Appl. doi: 10.1016/j.eswa.2021.115019 – ident: ref_31 – volume: 6 start-page: 1139 year: 2020 ident: ref_38 article-title: Deep-Learning-Based Optimization of the Under-Sampling Pattern in MRI publication-title: IEEE Trans. Comput. Imaging doi: 10.1109/TCI.2020.3006727 – volume: 40 start-page: 947 year: 2022 ident: ref_16 article-title: Prediction Model for Coronavirus Pandemic Using Deep Learning publication-title: Comput. Syst. Sci. Eng. doi: 10.32604/csse.2022.019288 – ident: ref_27 – volume: 80 start-page: 323 year: 2017 ident: ref_32 article-title: Multi-class sentiment classification: The experimental comparisons of feature selection and machine learning algorithms publication-title: Expert Syst. Appl. doi: 10.1016/j.eswa.2017.03.042 – ident: ref_46 – ident: ref_10 – volume: 2022 start-page: 7028717 year: 2022 ident: ref_23 article-title: Superlative Feature Selection Based Image Classification Using Deep Learning in Medical Imaging publication-title: J. Healthc. Eng. doi: 10.1155/2022/7028717 – ident: ref_37 doi: 10.1063/1.4994463 – ident: ref_41 doi: 10.1007/978-3-642-37256-8_2 – volume: 10 start-page: 22260 year: 2022 ident: ref_35 article-title: Sentiment Analysis of Customers’ Reviews Using a Hybrid Evolutionary SVM-Based Approach in an Imbalanced Data Distribution publication-title: IEEE Access doi: 10.1109/ACCESS.2022.3149482 – ident: ref_6 doi: 10.1162/COLI_a_00049 – volume: 2 start-page: 1 year: 2008 ident: ref_5 article-title: Opinion Mining and Sentiment Analysis publication-title: Found. Trends Inf. Retr. doi: 10.1561/1500000011 – volume: 138 start-page: 112834 year: 2019 ident: ref_12 article-title: Fuzzy rule based unsupervised sentiment analysis from social media posts publication-title: Expert Syst. Appl. doi: 10.1016/j.eswa.2019.112834 – ident: ref_44 doi: 10.1109/ICAwST.2019.8923260 – ident: ref_24 doi: 10.3390/electronics11193058 – ident: ref_43 – volume: 2020 start-page: 8829432 year: 2020 ident: ref_30 article-title: A Decoupling and Bidirectional Resampling Method for Multilabel Classification of Imbalanced Data with Label Concurrence publication-title: Sci. Program. – volume: 5 start-page: 1093 year: 2014 ident: ref_1 article-title: Sentiment analysis algorithms and applications: A survey publication-title: Ain Shams Eng. J. doi: 10.1016/j.asej.2014.04.011 – ident: ref_25 doi: 10.3390/mca23010011 – ident: ref_11 doi: 10.1371/journal.pone.0245909 – ident: ref_28 doi: 10.1109/ICAwST.2019.8923218 – ident: ref_34 doi: 10.3390/info9040100 |
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| Title | Enhancing Sentiment Analysis via Random Majority Under-Sampling with Reduced Time Complexity for Classifying Tweet Reviews |
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