Design Text Mining Classifier for Covid-19 by using the Machine Learning Techniques
Keywords:
COVID-19, machine learning classifier, supervised learning, feature extraction, feature selection, NLP, SVMAbstract
In the starting of year 2020, WHO identified COVID-19 as a new pandemic and issued a statement to that effect. This fatal virus was able to disperse and propagate over several nations all over the globe. During the course of the epidemic, social media platforms like Twitter generated significant and substantial volumes of data that helped improve the quality of decisions pertaining to health care. As a result, we suggest that the opinion expressed by users might be analysed via the use of efficient Supervised Machine Learning (SML) algorithms to forecast the occurrence of illness and offer early warnings. In this paper we proposed a text mining classifier for generate the summarized text using machine learning techniques. After collecting the tweets, we got them ready for pre-processing and generate the class label for all instances such as correct, incorrect and neutral etc. In the second phase, numerous features are extracted from text by using a number of frequently used approaches, such as TF-IDF, co-relational, NLP and relational dependency features are extracted to generate the feature vector. As classification module we used one binary classification algorithm and five machine learning algorithms for evaluation of proposed model. NLP-SVM and TFIDF-SVM produces higher accuracy 95.10% and 93.50% classification accuracy respectively. This demonstrates the proposed model is effective for classification of large text for COVID-19 on tweet data.
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. Li, X.; Zhang, J.; Du, Y.; Zhu, J.; Fan, Y.; Chen, X. A Novel Deep Learning-based Sentiment Analysis Method Enhanced with Emojis in Microblog Social Networks. Enterp. Inf. Syst. 2022, 1–22.
. Balli, C.; Guzel, M.S.; Bostanci, E.; Mishra, A. Sentimental Analysis of Twitter Users from Turkish Content with Natural Language Processing. Comput. Intell. Neurosci. 2022, 2022, 2455160.
. Zemberek, NLP Tools for Turkish. Available online: https://github.com/ahmetaa/zemberek-nlp (accessed on 20 September 2021).
. Sitaula, C.; Shahi, T.B. Multi-channel CNN to classify nepali COVID-19 related tweets using hybrid features. arXiv 2022, arXiv:2203.10286.
. Singh, C.; Imam, T.; Wibowo, S.; Grandhi, S. A Deep Learning Approach for Sentiment Analysis of COVID-19 Reviews. Appl. Sci. 2022, 12, 3709.
. Parimala, M.; Swarna Priya, R.; Praveen Kumar Reddy, M.; Lal Chowdhary, C.; Kumar Poluru, R.; Khan, S. Spatiotemporal-based sentiment analysis on tweets for risk assessment of event using deep learning approach. Softw. Pract. Exp. 2021, 51, 550–570.
. Kabir, M.; Madria, S. CoronaVis: A real-time COVID-19 tweets data analyzer and data repository. arXiv 2020, arXiv:2004.13932.
. Taboada, M. Sentiment analysis: An overview from linguistics. Annu. Rev. Linguist. 2016, 2, 325–347.
. Beigi, G.; Hu, X.; Maciejewski, R.; Liu, H. An overview of sentiment analysis in social media and its applications in disaster relief. In Sentiment Analysis and Ontology Engineering; Springer: Berlin/Heidelberg, Germany, 2016; pp. 313–340.
. Sailunaz, K.; Alhajj, R. Emotion and sentiment analysis from Twitter text. J. Comput. Sci. 2019, 36, 101003.
. Samuel, J.; Ali, G.; Rahman, M.; Esawi, E.; Samuel, Y. COVID-19 public sentiment insights and machine learning for tweets classification. Information 2020, 11, 314.
. Liu, R.; Shi, Y.; Ji, C.; Jia, M. A survey of sentiment analysis based on transfer learning. IEEE Access 2019, 7, 85401–85412.
. Tyagi, P.; Tripathi, R. A review towards the sentiment analysis techniques for the analysis of twitter data. In Proceedings of the 2nd International Conference on Advanced Computing and Software Engineering (ICACSE), Sultanpur, India, 8–9 February 2019.
. Saura, J.R.; Palacios-Marqués, D.; Ribeiro-Soriano, D. Exploring the boundaries of open innovation: Evidence from social media mining. Technovation 2022, 102447.
. Mackey, T.; Purushothaman, V.; Li, J.; Shah, N.; Nali, M.; Bardier, C.; Liang, B.; Cai, M.; Cuomo, R. Machine learning to detect self-reporting of symptoms, testing access, and recovery associated with COVID-19 on Twitter: Retrospective big data infoveillance study. JMIR Public Health Surveill. 2020, 6, e19509.
. Wan, S.; Yi, Q.; Fan, S.; Lv, J.; Zhang, X.; Guo, L.; Lang, C.; Xiao, Q.; Xiao, K.; Yi, Z.; et al. Relationships among lymphocyte subsets, cytokines, and the pulmonary inflammation index in coronavirus (COVID-19) infected patients. Br. J. Haematol. 2020, 189, 428–437.
. Rajput, N.K.; Grover, B.A.; Rathi, V.K. Word frequency and sentiment analysis of twitter messages during coronavirus pandemic. arXiv 2020, arXiv:2004.03925.
. Muthusami, R.; Bharathi, A.; Saritha, K. COVID-19 outbreak: Tweet based analysis and visualization towards the influence of coronavirus in the world. Gedrag Organ. Rev. 2020, 33, 8–9.
. Jelodar, H.; Wang, Y.; Orji, R.; Huang, S. Deep sentiment classification and topic discovery on novel coronavirus or COVID-19 online discussions: Nlp using lstm recurrent neural network approach. IEEE J. Biomed. Health Inform. 2020, 24, 2733–2742.
. Aljameel, S.S.; Alabbad, D.A.; Alzahrani, N.A.; Alqarni, S.M.; Alamoudi, F.A.; Babili, L.M.; Aljaafary, S.K.; Alshamrani, F.M. A sentiment analysis approach to predict an individual’s awareness of the precautionary procedures to prevent COVID-19 outbreaks in Saudi Arabia. Int. J. Environ. Res. Public Health 2021, 18, 218.
. Ghadeer, A.S.; Aljarah, I.; Alsawalqah, H. Enhancing the Arabic sentiment analysis using different preprocessing operators. New Trends Inf. Technol. 2017, 113, 113–117.
. Imran, A.S.; Daudpota, S.M.; Kastrati, Z.; Batra, R. Cross-cultural polarity and emotion detection using sentiment analysis and deep learning on COVID-19 related tweets. IEEE Access 2020, 8, 181074–181090.
. Alam, F.; Dalvi, F.; Shaar, S.; Durrani, N.; Mubarak, H.; Nikolov, A.; Martino, G.D.S.; Abdelali, A.; Sajjad, H.; Darwish, K.; et al. Fighting the COVID-19 infodemic in social media: A holistic perspective and a call to arms. arXiv 2020, arXiv:2007.07996.
. Alqurashi, S.; Hamoui, B.; Alashaikh, A.; Alhindi, A.; Alanazi, E. Eating garlic prevents COVID-19 infection: Detecting misinformation on the arabic content of twitter. arXiv 2021, arXiv:2101.05626.
. Naseem, U.; Razzak, I.; Khushi, M.; Eklund, P.W.; Kim, J. Covidsenti: A large-scale benchmark Twitter data set for COVID-19 sentiment analysis. IEEE Trans. Comput. Soc. Syst. 2021, 8, 1003–1015.
. Basiri, M.E.; Nemati, S.; Abdar, M.; Asadi, S.; Acharrya, U.R. A novel fusion-based deep learning model for sentiment analysis of COVID-19 tweets. Knowl.-Based Syst. 2021, 228, 107242.
. Rustam, F.; Khalid, M.; Aslam, W.; Rupapara, V.; Mehmood, A.; Choi, G.S. A performance comparison of supervised machine learning models for COVID-19 tweets sentiment analysis. PLoS ONE 2021, 16, e0245909.
. Nemes, L.; Kiss, A. Social media sentiment analysis based on COVID-19. J. Inf. Telecommun. 2021, 5, 1–15.
. Vel SS. Pre-Processing techniques of Text Mining using Computational Linguistics and Python Libraries. In2021 International Conference on Artificial Intelligence and Smart Systems (ICAIS) 2021 Mar 25 (pp. 879-884). IEEE.
. Kaur, H.; Ahsaan, S.U.; Alankar, B.; Chang, V. A proposed sentiment analysis deep learning algorithm for analyzing COVID-19 tweets. Inf. Syst. Front. 2021, 23, 1417–1429.
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