• P-ISSN 0974-6846 E-ISSN 0974-5645

Indian Journal of Science and Technology


Indian Journal of Science and Technology

Year: 2024, Volume: 17, Issue: 7, Pages: 610-616

Original Article

A Hybrid Approach to Analyse the Public Sentiment on Covid-19 Tweets

Received Date:26 November 2023, Accepted Date:13 January 2024, Published Date:08 February 2024


Objectives: The objective of this study is to introduce a hybrid model for analyzing the people sentiment on covid-19 tweets. Methods: We used a total no. of 27,500 datasets, 70% of the data sets for training and reserved the other 30% for testing. Due to this separation 19,250 samples are used for training, the remaining 8,250 were used to evaluate the accuracy of the test. This paper proposes a technique for sentiment analysis that integrates deep learning, genetic algorithms (GA), and social media sentiment. For more accuracy and performance, we here suggested a hybrid genetic algorithm-based model. A hybrid model is created by assembling the LSTM model and providing it to the genetic algorithm architecture. Findings: LSTM with a genetic model better than LSTM without genetic model. The accuracy of our suggested model is 96.40%. Novelty : The accuracy of the LSTM model for sentiment analysis is 91%. The accuracy of the proposed model is 96.40%. The proposed model is more accurate for sentiment prediction.

Keywords: Social network perception, Crossover, Mutation, LSTM, NLP, GA


  1. Jana RK, Maity S. An Accuracy Based Comparative Study on Different Techniques and Challenges for Sentiment Analysis. In: Pervasive Computing and Social Networking, Lecture Notes in Networks and Systems. (Vol. 475, pp. 601-619) Singapore. Springer. 2023.
  2. Ahmed C, Elkorany A, Elsayed E. Prediction of customer’s perception in social networks by integrating sentiment analysis and machine learning. Journal of Intelligent Information Systems. 2023;60(3):829–851. Available from: https://doi.org/10.1007/s10844-022-00756-y
  3. Sanwal T, Yadav S, Avasthi S, Prakash A, Tyagi M. Social Media and Networking Applications in the Education Sector. In: 2023 2nd Edition of IEEE Delhi Section Flagship Conference (DELCON). (pp. 1-6) IEEE. 2023.
  4. Katoch S, Chauhan SS, Kumar V. A review on genetic algorithm: past, present, and future. Multimedia tools and applications. 2021;80:8091–8126. Available from: https://doi.org/10.1007/s11042-020-10139-6
  5. Sangule S, Phulre S. Sentiment Detection Using Genetic Feature Vector And Neural Network Model. International Journal of Advanced Research in Engineering and Technology (IJARET). 2020;11(12):2726–2734. Available from: https://iaeme.com/MasterAdmin/Journal_uploads/IJARET/VOLUME_11_ISSUE_12/IJARET_11_12_257.pdf
  6. Yuan FC, Lee CH, Chiu C. Using Market Sentiment Analysis and Genetic Algorithm-Based Least Squares Support Vector Regression to Predict Gold Prices. International Journal of Computational Intelligence Systems. 2020;13(1):234–246. Available from: https://doi.org/10.2991/ijcis.d.200214.002
  7. Merlin DJA, Kumar DV. Perceptive Genetic Algorithm-Based Wolf Inspired Classifier For Big Sentiment Data Analysis. Journal Of Theoretical And Applied Information Technology. 2022;100(16):5021–5031. Available from: https://www.jatit.org/volumes/Vol100No16/13Vol100No16.pdf
  8. Rafdi A, Mawengkang H, Efendi S. Sentiment Analysis Using Naive Bayes Algorithm with Feature Selection Particle Swarm Optimization (PSO) and Genetic Algorithm. International Journal of Advances in Data and Information Systems. 2021;2(2):96–104. Available from: https://doi.org/10.25008/ijadis.v2i2.1224
  9. Al-Qudah DA, Al-Zoubi AM, Castillo-Valdivieso PA, Faris H. Sentiment Analysis for e-Payment Service Providers Using Evolutionary eXtreme Gradient Boosting. IEEE Access . 2020;8:189930–189944. Available from: https://doi.org/10.1109/ACCESS.2020.3032216
  10. Al-Shabi MA. Evaluating the performance of the most important Lexicons used to Sentiment analysis and opinions Mining. International Journal of Computer Science and Network Security. 2020;20(1):51–57. Available from: http://paper.ijcsns.org/07_book/202001/20200107.pdf
  11. Aryanti R, Saryoko A, Junaidi A, Marlina S, Wahyudin, Nurmalia L. Comparing Classification Algorithm With Genetic Algorithm In Public Transport Analysis. In: International Conference on Advanced Information Scientific Development (ICAISD) , Journal of Physics: Conference Series. (Vol. 1641, pp. 1-6) IOP Publishing. 2020.
  12. Rani P, Shokeen J, Majithia A, Agarwal A, Bhatghare A, Malhotra J. Designing an LSTM and Genetic Algorithm-based Sentiment Analysis Model for COVID-19. In: Proceedings of Data Analytics and Management, Lecture Notes on Data Engineering and Communications Technologies . (Vol. 91, pp. 209-216) Singapore. Springer . 2022.
  13. Sravya G, Sreedevi M. Genetic Optimization in Hybrid Level Sentiment Analysis for Opinion Classification. International Journal of Advanced Trends in Computer Science and Engineering. 2020;9(2):1440–1445. Available from: https://www.warse.org/IJATCSE/static/pdf/file/ijatcse81922020.pdf
  14. Wang J, Fan Y, Palacios J, Chai Y, Guetta-Jeanrenaud N, Obradovich N, et al. Global evidence of expressed sentiment alterations during the COVID-19 pandemic. Nature Human Behaviour. 2022;6(3):349–358. Available from: https://doi.org/10.1038/s41562-022-01312-y
  15. Luu T(P, Follmann R. The relationship between sentiment score and COVID-19 cases in the United States. Journal of Information Science. 2023;49(6):1615–1630. Available from: https://doi.org/10.1177/01655515211068167
  16. Arbane M, Benlamri R, Brik Y, Alahmar AD. Social media-based COVID-19 sentiment classification model using Bi-LSTM. Expert Systems with Applications. 2023;212:1–9. Available from: https://doi.org/10.1016/j.eswa.2022.118710
  17. Bashar MK. A Hybrid Approach to Explore Public Sentiments on COVID-19. SN Computer Science. 2022;3(3):1–19. Available from: https://doi.org/10.1007/s42979-022-01112-1
  18. Gupta VK, Gupta AK, Kumar D, Sardana A. Prediction of COVID-19 confirmed, death, and cured cases in India using random forest model. Big Data Mining and Analytics. 2021;4(2):116–123. Available from: https://doi.org/10.26599/BDMA.2020.9020016
  19. Chai Y, Kakkar D, Palacios J, Zheng S. Twitter Sentiment Geographical Index Dataset. Scientific Data. 2023;10(1):1–12. Available from: https://doi.org/10.1038/s41597-023-02572-7
  20. Parvin SA, Sumathi M, Barani R. A Novel Approach to Classify Sentiments on Different Datasets Using Hybrid Approaches of Sentiment Analysis. Indian Journal of Science and Technology. 2023;16(44):3962–3970. Available from: https://doi.org/ 10.17485/IJST/v16i44.2498
  21. Al-Harbi O, Hamed A, Alzoubi M. A Deep Neural Network Optimized by a Genetic Algorithm to Improve Arabic Sentiment Classification. Ingénierie des systèmes d information. 2023;28(1):67–75. Available from: https://doi.org/10.18280/isi.280107
  22. Tripathy G, Sharaff A. AEGA: enhanced feature selection based on ANOVA and extended genetic algorithm for online customer review analysis. The Journal of Supercomputing. 2023;79(12):13180–13209. Available from: https://doi.org/10.1007/s11227-023-05179-2


© 2024 Jana et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Published By Indian Society for Education and Environment (iSee)


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