International Journal of Engineering Research in Computer Science and Engineering (IJERCSE)

Sentiment Analysis of Covid-19 Tweets using Various Machine Learning Techniques with Hyperparameter Tuning on Twitter Database

Author : Tejaswini Zope ¹ Dr. K .Rajeswari ²

PDF

Date of Publication :1st November 2022

Abstract: People around the world are now being affected by the 2019 coronavirus disease (COVID-19) epidemic. People are using social media to express their opinions and general thoughts about the epidemic, which has affected their daily lives both in general and during lockdown periods. Twitter, one of the most used social media platforms, has seen a massive surge in tweets related to the coronavirus, including happy, sad, fear, and anger tweets, in a very short period. In the COVID-19 tweet dataset, there is a total number of tweets count is 179108 data in unstructured form, and after that preprocess that data to become semi structured form. After preprocessing feature extraction is used by applying CountVecotorizer and TF-IDF methods. Various Machine Learning Models such as Random Forest (RF), Decision Tree (DT), Support Vector Machine (SVM), and Stochastic Gradient Decent (SGD) are considered machine learning methods for sentiment analysis, To improve performance (accuracy), with Hyperparameters tuning method is used.

Reference :

1. ANAM YOUSAF,MUHAMMAD UMER,SAIMA SADIQ ” Emotion Recognition by Textual Tweets Classification Using Voting Classifier (LR-SGD)” Digital Object Identifier 10.1109/ACCESS.2020.3047831
2. Chaudhary Jagrit Varshney,Dr. Ashish Sharma, Dhirendra Prasad Yadav” Sentiment Analysis using Ensemble Classification Technique” 2020 IEEE Studentsâ€Ÿ Conference on Engineering & Systems (SCES) July 10-12, 2020
3. Nilufa Yeasmin, Nosin Ibna Mahbub, Mrinal Kanti Baowaly, Bikash Chandra Singh” Analysis and Prediction of User Sentiment on COVID-19 Pandemic Using Tweets” Big Data Cogn. Comput. 2022, 6, 65.
4. Babacar Gaye, Dezheng Zhang and Aziguli Wulamu ”Sentiment classification for employees reviews using regression vectorstochastic gradient descent classifier (RVSGDC)” DOI 10.7717/peerj-cs.712
5. V. Vaithiyanathan, K. Rajeswari, Kapil Tajane, Rahul Pitale, “COMPARISON OF DIFFERENT CLASSIFICATION TECHNIQUES USING DIFFERENT DATASETS”,in 2003 May IJEAT, Vol. 6, Issue 2, pp. 764-768
6. M. A. Hearst, S. T. Dumais, E. Osuna, J. Platt and B. Scholkopf, "Support vector machines," IEEE Intell. Syst. Appl., vol. 13, no. 4, pp. 18-28, July- Aug. 1998, DOI: 10.1109/5254.708428.
7. Garima Koushik, Dr. K. Rajeswari, Mr. Suresh Kannan Muthusamy, “Automated Hate Speech Detection on Twitter”, in 2019 IEEE conference, 978-1-7281-4042-1/19/$31.00
8. Bijari, Kayvan et al. “Leveraging Deep Graph-Based Text Representation for Sentiment Polarity Applications.” Expert Systems with Applications 144 (2020): 113090. Crossref. Web.
9. Tripathy, Abinash, A. Agrawal, and S. K. Rath. "Classification of Sentimental Reviews Using Machine Learning Techniques." Procedia Computer Science 57(2015):821-829.
10. Davidov, Dmitry, O. Tsur, and A. Rappoport. "Semisupervised recognition of sarcastic sentences in Twitter and Amazon." Conll(2010):107-116
11. P. Routray, C. K. Swain, and S. P. Mishra, ``A survey on sentiment analysis,'' Int. J. Comput. Appl., vol. 76, no. 10, pp. 18, Aug. 2013.
12. A. Harb, M. Plantié, G. Dray, M. Roche, F. Trousset, and P. Poncelet, ``Web opinion mining: How to extract opinions from blogs?'' in Proc. 5th Int.Conf. Soft Comput. Transdisciplinary Sci. Technol. (CSTST). New York,NY, USA: Association for Computing Machinery, 2008. pp. 211217.
13. B. Pang, L. Lee, and S. Vaithyanathan, ``Thumbs up? Sentiment classification using machine learning techniques,'' EMNLP, vol. 10, pp. 1-9,Jun. 2002.
14. K. P. Bennett and C. Campbell, ``Support vector machines: hype or hallelujah?''Acm Sigkdd Explor. Newslett., vol. 2, no. 2, pp. 1-13, 2000.
15. Rezaeinia SM, Rahmani R, Ghodsi A, Veisi H. 2019. Sentiment analysis based on improved pre-trained word embeddings. Expert Systems with Applications 117:139_147 DOI 10.1016/j.eswa.2018.08.044.