Synthesis of Slogans with Predicted Sentiment from Twitter using a Novel Hybrid SDG-LSTM Model for Election Campaigns

Shailesh S Sangle; Raghavendra R Sedamkar

doi:10.17485/IJST/v17i14.118

Article

Synthesis of Slogans with Predicted Sentiment from Twitter using a Novel Hybrid SDG-LSTM Model for Election Campaigns

VIEWS 76
PDF 25

Indian Journal of Science and Technology

DOI: 10.17485/IJST/v17i14.118

Year: 2024, Volume: 17, Issue: 14, Pages: 1474-1484

Original Article

Synthesis of Slogans with Predicted Sentiment from Twitter using a Novel Hybrid SDG-LSTM Model for Election Campaigns

Shailesh S Sangle^1*, Raghavendra R Sedamkar²

¹Research Scholor, Computer Engineering Department, Thadomal Shahani College of Engineering, Bandra (W), Mumbai, Maharashtra, India
²Professor, Computer Engineering Department, Thakur College of Engineering and Technology, Kandivali (E), Mumbai, Maharashtra, India

*Corresponding Author
Email: [email protected]

Received Date:22 January 2024, Accepted Date:08 March 2024, Published Date:03 April 2024

This work is licensed under a Creative Commons Attribution 4.0 International License.

Abstract

Objectives: The primary objectives of this study encompass the enhancement of election campaign strategies through the synthesis of sentiment-laden slogans derived from Twitter data. This is achieved by employing a novel Hybrid SDG-LSTM model, aiming to improve sentiment prediction accuracy and communication efficacy in the context of political campaigns. Methods: The process of slogan generation relies on sentiment prediction derived from sentiment-laden tweets. The proposed sentiment analysis methods for election campaign slogans encompass Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU). A novel approach is introduced through the Hybrid SDG-LSTM model, leveraging the combination of Self-Distillation Guidance (SDG) with LSTM to enhance sentiment prediction accuracy and efficiency. This innovative method aims to provide a more robust and effective means of analyzing and generating slogans for election campaigns. Findings: The performance assessment of Deep Learning models, GRU, LSTM, and the Hybrid architecture, unveiled compelling outcomes. GRU showcased a commendable accuracy of 92.98%, while LSTM impressed with 95.91%. Remarkably, the Hybrid Spatial LSTM with GRU surpassed both, achieving perfection with 100% accuracy, precision, recall, and an exceptionally low loss of 0.0. These results underscore the superior performance and efficacy of the Hybrid model in sentiment analysis tasks. Novelty: The novelty of this research is encapsulated in the introduction of the Hybrid Spatial LSTM with GRU model, which demonstrates groundbreaking 100% accuracy, surpassing current models. This innovation capitalizes on the synergistic fusion of spatial attention mechanisms and the dynamic nature of GRU, marking a substantial advancement and establishing a new benchmark for highly accurate predictions in the domain of sentiment analysis.

Keywords: Slogan Generation, Sentiment Analysis, Election Campaign, Deep Learning, LSTM, GRU

References

Goswami A, Krishna MM, Vankara J, Gangadharan SMP, Yadav CS, Kumar M, et al. Sentiment Analysis of Statements on Social Media and Electronic Media Using Machine and Deep Learning Classifiers. Computational Intelligence and Neuroscience. 2022;2022:1–18. doi: 10.1155/2022/9194031
Chauhan P, Sharma N, Sikka G. Application of Twitter sentiment analysis in election prediction: a case study of 2019 Indian general election. Social Network Analysis and Mining. 2023;13(1). Available from: https://doi.org/10.1007/s13278-023-01087-8
Venkatesan D, Kannan SK, Arif M, Atif M, Ganeshan A. Sentimental Analysis of Industry 4.0 Perspectives Using a Graph-Based Bi-LSTM CNN Model. Mobile Information Systems. 2022;2022:1–14. doi: 10.1155/2022/5430569
Aljehane NO. A New Approach to Sentiment Analysis on Twitter Data with LSTM. In: 2023 3rd International Conference on Computing and Information Technology (ICCIT). (pp. 657-663) IEEE. 2023. 10.1109/ICCIT58132.2023.10273876
Diekson ZA, Prakoso MRB, Putra MSQ, Syaputra MSAF, Achmad S, Sutoyo R. Sentiment analysis for customer review: Case study of Traveloka. Procedia Computer Science. 2023;216:682–690. doi: 10.1016/j.procs.2022.12.184
Yao G. Deep Learning-Based Text Sentiment Analysis in Chinese International Promotion. Security and Communication Networks. 2022;2022:1–10. doi: 10.1155/2022/7319656
Yan J, Ma X. Microblog Emotion Analysis Method Using Deep Learning in Spark Big Data Environment. Mobile Information Systems. 2022;2022:1–9. doi: 10.1155/2022/1909312
Sangle SS, Sedamkar RR. NLP-Based Sentiment Analysis with Machine Learning Model for Election Campaign—A Survey. In: Third Congress on Intelligent Systems, Lecture Notes in Networks and Systems. (Vol. 613, pp. 595-612) Singapore. Springer . 2023. 10.1007/978-981-19-9379-4_43
Alvi Q, Ali SF, Ahmed SB, Khan NA, Javed M, Nobanee H. On the frontiers of Twitter data and sentiment analysis in election prediction: a review. PeerJ Computer Science. 2023;9:1–25. doi: 10.7717/peerj-cs.1517
Al-Abyadh MHA, Iesa MAM, Azeem HAHA, Singh DP, Kumar P, Abdulamir M, et al. Deep Sentiment Analysis of Twitter Data Using a Hybrid Ghost Convolution Neural Network Model. Computational Intelligence and Neuroscience. 2022;2022:1–8. doi: 10.1155/2022/6595799
Chen G. Research on Sentiment Analysis of Online Product Reviews Based on Deep Learning. In: Proceedings of the 2022 6th International Conference on Computer Science and Artificial Intelligence. (Vol. 2022, pp. 184-188) ACM. 2022. 10.1145/3577530.3577560
Zhu Z. Deep Learning for Chinese Language Sentiment Extraction and Analysis. Mathematical Problems in Engineering. 2022;2022:1–12. doi: 10.1155/2022/8145445
Dang CN, Moreno-García MN, Prieta FDl. Hybrid Deep Learning Models for Sentiment Analysis. Complexity. 2021;2021:1–16. doi: 10.1155/2021/9986920
Singh A, Kumar A, Dua N, Mishra VK, Singh D, Agrawal A. Predicting Elections Results using Social Media Activity A Case Study: USA Presidential Election 2020. In: 2021 7th International Conference on Advanced Computing and Communication Systems (ICACCS). (pp. 314-319) IEEE. 2021. 10.1109/ICACCS51430.2021.9441835
Ansari MZ, Aziz MB, Siddiqui MO, Mehra H, Singh KP. Analysis of Political Sentiment Orientations on Twitter. Procedia Computer Science. 2020;167:1821–1828. doi: 10.1016/j.procs.2020.03.201
Haque R, Islam N, Tasneem M, Das AK. Multi-class sentiment classification on Bengali social media comments using machine learning. International Journal of Cognitive Computing in Engineering. 2023;4:21–35. doi: 10.1016/j.ijcce.2023.01.001
Aslan S. A novel TCNN-Bi-LSTM deep learning model for predicting sentiments of tweets about COVID-19 vaccines. Concurrency and Computation: Practice and Experience. 2022;34(28). doi: 10.1002/cpe.7387
Gaye B, Zhang D, Wulamu A. A Tweet Sentiment Classification Approach Using a Hybrid Stacked Ensemble Technique. Information. 2021;12(9):1–19. doi: 10.3390/info12090374
Kokab ST, Asghar S, Naz S. Transformer-based deep learning models for the sentiment analysis of social media data. Array. 2022;14:1–12. doi: 10.1016/j.array.2022.100157
Chakraborty K, Bhatia S, Bhattacharyya S, Platos J, Bag R, Hassanien AE. Sentiment Analysis of COVID-19 tweets by Deep Learning Classifiers—A study to show how popularity is affecting accuracy in social media. Applied Soft Computing. 2020;97(Part A):1–14. doi: 10.1016/j.asoc.2020.106754
Yero EJH, Sacco NC, Nicoletti MdC. Effect of the Municipal Human Development Index on the results of the 2018 Brazilian presidential elections. Expert Systems with Applications. 2021;168:114305. doi: 10.1016/j.eswa.2020.114305
Abonyi J, Károly R, Dörgö G. Event-Tree Based Sequence Mining Using LSTM Deep-Learning Model. Complexity. 2021;2021:1–24. doi: 10.1155/2021/7887159
Tsai MHH, Wang Y, Kwak M, Rigole N. A Machine Learning Based Strategy for Election Result Prediction. In: 2019 International Conference on Computational Science and Computational Intelligence (CSCI). (pp. 1408-1410) IEEE. 2020. 10.1109/CSCI49370.2019.00263
Ali H, Farman H, Yar H, Khan Z, Habib S, Ammar A. Deep learning-based election results prediction using Twitter activity. Soft Computing. 2022;26(16):7535–7543. doi: 10.1007/s00500-021-06569-5
Hidayatullah AF, Cahyaningtyas S, Hakim AM. Sentiment Analysis on Twitter using Neural Network: Indonesian Presidential Election 2019 Dataset. In: The 5th International Conference on Information Technology and Digital Applications (ICITDA 2020), IOP Conference Series: Materials Science and Engineering. (Vol. 1077, pp. 1-6) IOP Publishing. 2021. 10.1088/1757-899x/1077/1/012001
Rodrigues AP, Fernandes R, Aakash A, Abhishek B, Shetty A, Atul K, et al. Real-Time Twitter Spam Detection and Sentiment Analysis using Machine Learning and Deep Learning Techniques. Computational Intelligence and Neuroscience. 2022;2022:1–14. doi: 10.1155/2022/5211949
Alam KN, Khan MS, Dhruba AR, Khan MM, Al-Amri JF, Masud M, et al. Deep Learning-Based Sentiment Analysis of COVID-19 Vaccination Responses from Twitter Data. Computational and Mathematical Methods in Medicine. 2021;2021:1–15. doi: 10.1155/2021/4321131

Copyright

© 2024 Sangle & Sedamkar. 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)