Sentiment Analysis of COVID 19 Tweets using Optimize LSTM Model
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The popularity of social media has increased curiosity in psychology, mental health, and human circumstances. Twitter and other social media platforms have been utilised for data collection, personality type prediction, and sentiment analysis during emergencies. Deep learning techniques examine both positive and negative feelings. The retrieval of Covid 19 tweets, both positive and negative, is not perfect, and low accuracy may lead to the detection of unidentified tweets from social sites. The objective is to improve the retrieval and comparison of pertinent Covid 19 positive and negative tweets in terms of precision, recall, and train/validation accuracy. A LSTM is defined by a sequence of layers, with the first layer defining inputs. The ADAM optimization algorithm updates weights, and the model is evaluated. The proposed model for sentiment content categorization from tweets uses LSTM-ADAM approaches, with a 78% accuracy rate compared to the 76% accuracy of SVM methodology. It supports manual participation and accepts text information in any format.
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Zimbra, D.; Abbasi, A.; Zeng, D.; Chen, H. The State-of-the-Art in Twitter Sentiment Analysis: A Review and Benchmark Evaluation. ACM Trans. Manag. Inf. Syst. (2018)
Bollen, J.; Mao, H.; Zeng, X. Twitter Mood Predicts the Stock Market. J. Comput. Sci. (2011).
Wang, H.; Can, D.; Kazemzadeh, A.; Bar, F.; Narayanan, S. A System for Real-time Twitter Sentiment Analysis of 2012 U.S. Presidential Election Cycle. In Proceedings of the ACL 2012 System Demonstrations, Jeju Island, Korea, 10 July (2012).
Meng, F.; Xiao, X.; Wang, J. Rating the crisis of online public opinion using a multi-level index system. Int. Arab. J. Inf. Techn. (2022)
Bing, L.; Lei, Z. A survey of opinion mining and sentiment analysis. In Mining Text Data, 1st ed.; Charu, C.A., ChengXiang, Z., Eds.; Springer: New York, NY, USA, (2012).
Adwan, O.; Al-Tawil, M.; Huneiti, A.; Shahin, R.; Zayed, A.A.; Al-Dibsi, R. Twitter sentiment analysis approaches: A survey. Int. J. Emerg. Technol. (2020)
Giachanou, A.; Crestani, F. Like it or not: A survey of Twitter sentiment analysis methods. ACM Comput. Surv. (CSUR) (2016)
Pak, A.; Paroubek, P. Twitter as a corpus for sentiment analysis and opinion mining. In Proceedings of the Seventh International Conference on Language Resources and Evaluation, Valletta, Malta, 17–23 May 2010. Appl. Sci. (2022)
Swathi, T.; Kasiviswanath, N.; Rao, A.A. An optimal deep learning-based LSTM for stock price prediction using twitter sentiment analysis. Appl. Intell. (2022)
Jose, A.K.; Bhatia, N.; Krishna, S. Twitter Sentiment Analysis; Seminar Report; National Institute of Technology Calicut: Kozhikode, India, (2010).
Gupta, D.K.; Reddy, K.S.; Ekbal, A. Pso-asent: Feature selection using particle swarm optimization for aspect based sentiment analysis. In Proceedings of the International Conference on Applications of Natural Language to Information Systems, Passau, Germany, 17 June (2015).
Unler, A.; Murat, A. A discrete particle swarm optimization method for feature selection in binary classification problems. Eur. J. Oper. Res. (2010)
Shang, L.; Zhou, Z.; Liu, X. Particle swarm optimization-based feature selection in sentiment classification. Soft Comput. (2016)
Liu, Z.; Liu, S.; Liu, L.; Sun, J.; Peng, X.; Wang, T. Sentiment recognition of online course reviews using multi-swarm optimization based selected features. Neurocomputing (2016)
Onan, A.; Koruko ˘glu, S.; Bulut, H. A multiobjective weighted voting ensemble classifier based on differential evolution algorithm for text sentiment classification. Expert Syst. Appl. (2016)
Basari, A.S.H.; Hussin, B.; Ananta, I.G.P.; Zeniarja, J. Opinion mining of movie review using hybrid method of support vector machine and particle swarm optimization. Procedia Eng. (2013)
Kummer, O.; Savoy, J. Feature Weighting Strategies in Sentiment Analysis. In Proceedings of the First International Workshop on Sentiment Discovery from Affective Data, Bristol, UK, 24–28 September (2012)
O’Keefe, T.; Koprinska, I. Feature selection and weighting methods in sentiment analysis. In Proceedings of the 14th Australasian Document Computing Symposium, Sydney, NSW, Australia, 4 December (2009)
Neviarouskaya, A.; Prendinger, H.; Ishizuka, M. Recognition of affect, judgment, and appreciation in text. In Proceedings of the 23rd International Conference on Computational Linguistics, Beijing, China, 23–27 August (2010).
Heerschop, B.; Goossen, F.; Hogenboom, A.; Frasincar, F.; Kaymak, U.; de Jong, F. Polarity analysis of texts using discourse structure. In Proceedings of the 20th ACM International Conference on Information and Knowledge Management, Glasgow, UK, 24–28 October (2011).
Moreo, A.; Romero, M.; Castro, J.L.; Zurita, J.M. Lexicon-based comments-oriented news sentiment analyzer system. Expert Syst. Appl. (2012)
Balahur, A.; Hermida, J.M.; Montoyo, A. Detecting implicit expressions of emotion in text: A comparative analysis. Decis. Support Syst. (2012)
Mohammad, S.M. From once upon a time to happily ever after: Tracking emotions in mail and books. Decis. Support Syst. (2012)
Ortega, R.; Fonseca, A.; Montoyo, A. SSA-UO: Unsupervised Twitter sentiment analysis. In Proceedings of the Second Joint Conference on Lexical and Computational Semantics, Atlanta, GA, USA, 13–14 June (2013)
Reckman, H.; Baird, C.; Crawford, J.; Crowell, R.; Micciulla, L.; Sethi, S.; Veress, F. teragram: Rule-based detection of sentiment phrases using sas sentiment analysis. In Proceedings of the Second Joint Conference on Lexical and Computational Semantics, Volume 2: Seventh International Workshop on Semantic Evaluation, Atlanta, GA, USA, 14–15 June 2013; Association for Computational Linguistic: Stroudsburg, PE, USA, (2013)
Hu, X.; Tang, J.; Gao, H.; Liu, H. Unsupervised sentiment analysis with emotional signals. In Proceedings of the 22nd International Conference on World Wide Web, Rio de Janeiro, Brazil, 13 May (2013)
Saif, H.; He, Y.; Fernandez, M.; Alani, H. Contextual semantics for sentiment analysis of Twitter. Inf. Process. Manag. (2016)
Feng, S.; Bose, R.; Choi, Y. Learning general connotation of words using graph-based algorithms. In Proceedings of the Conference on Empirical Methods in Natural Language Processing, Edinburgh, UK, 27–31 July (2011)
Chen, L.S.; Liu, C.H.; Chiu, H.J. A neural network based approach for sentiment classification in the blogosphere. J. Informetr. (2011)
Xia, R.; Zong, C.; Li, S. Ensemble of feature sets and classification algorithms for sentiment classification. Inform. Sci. (2011)
Abdul-Mageed, M.; Diab, M.; Kübler, S. SAMAR: Subjectivity and sentiment analysis for Arabic social media. Comput. Speech Lang (2014)
Abbasi, A.; France, S.; Zhang, Z.; Chen, H. Selecting attributes for sentiment classification using feature relation networks. IEEE Trans. Knowl. Data Eng. (2011)
Prabowo, R.; Thelwall, M. Sentiment analysis: A combined approach. J. Informetr. (2009)
Zhang, L.; Ghosh, R.; Dekhil, M.; Hsu, M.; Liu, B. Combining Lexicon-Based and Learning-Based Methods for Twitter Sentiment Analysis; Technical Report HPL-2011-89; Hewlett-Packard Development Company: Palo Alto, CA, USA, (2011)
Ghiassi, M.; Skinner, J.; Zimbra, D. Twitter brand sentiment analysis: A hybrid system using n-gram analysis and dynamic artificial neural network. Expert Syst. Appl. (2013)
Khuc, V.N.; Shivade, C.; Ramnath, R.; Ramanathan, J. Towards building large-scale distributed systems for Twitter sentiment analysis. In Proceedings of the 27th Annual ACM Symposium on Applied Computing, Trento, Italy, 26–30 March (2012)
Khan, F.H.; Bashir, S.; Qamar, U. TOM, Twitter opinion mining framework using hybrid classification scheme. Decis. Support Syst. (2014)
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