International Journal of Image, Graphics and Signal Processing (IJIGSP)
IJIGSP Vol. 17, No. 3, 8 Jun. 2025
Cover page and Table of Contents: PDF (size: 923KB)
Discriminative Sentimental NLP Model with Re-Enforcement Deep Learning Model for the Slogan Generation
PDF (923KB), PP.34-54
Views: 0 Downloads: 0
Author(s)
Index Terms
Election Campaign, Natural Language Processing (NLP), Sentimental Analysis, Slogan, Discriminative Analysis, Conditional Random Field
Abstract
Effective communication is paramount in election campaigns, and slogans are crucial in conveying messages and eliciting voter sentiment. This paper introduces CRFVReC (Conditional Random Field with Variable-Length Receptive Fields), a statistical modeling technique to categorize and generate election campaign slogans by analyzing their sentiment. It is a novel approach for sentiment-based slogan generation and analysis in election campaigns. The reason was to choose datasets that precisely capture voter sentiment from a variety of sources such as social media (SM) posts, public comments, and news articles. The datasets were meticulously chosen to encompass a broad spectrum of sentiments and issues that are pertinent to voters. The CRFVReC model was set up to maximize the performance of sentiment classification and slogan generation. Modifying parameters such as the length of the receptive field to match the length of slogans enhanced the model's adaptability and increased its accuracy. Utilizing Conditional Random Fields (CRFs), CRFVReC classifies election campaign slogans into optimistic and pessimistic sentiments and generates slogans that resonate emotionally with voters. The key objectives of this study are twofold: first, to accurately classify election campaign slogans into two primary sentiment categories, optimistic and pessimistic, and second, to generate emotionally resonant slogans that can effectively connect with voters. Extensive experiments and sentiment analyses are conducted using a diverse dataset of election campaign slogans to assess the efficiency of CRFVReC. The results highlight the model's remarkable precision in sentiment classification, demonstrating its capability to discriminate between optimistic and pessimistic sentiment in slogans. The model exhibits elevated accuracy, precision, recall, and AUC scores in sentiment classification, utilizing a diverse dataset. Furthermore, CRFVReC showcases its creative potential in generating slogans with emotionally compelling content. Its capability holds significant promise for campaign strategists and political communicators seeking to craft slogans that resonate with voters deeply emotionally. Additionally, the model's adaptability to slogans of varying lengths makes it a versatile tool for election campaign management and strategy development. The CRFVReC emerges as a robust and adaptable solution for sentiment-based slogan generation and analysis in the complex landscape of election campaigns. Its contributions lie not only in inaccurate sentiment classification but also in its potential to shape the narrative of political campaigns through the creation of emotionally impactful slogans. This research contributes to the fields of political communication and campaign management, providing valuable tools and insights for practitioners and researchers.
Cite This Paper
Shailesh S. Sangle, Raghavendra R. Sedamkar, "Discriminative Sentimental NLP Model with Re-Enforcement Deep Learning Model for the Slogan Generation", International Journal of Image, Graphics and Signal Processing(IJIGSP), Vol.17, No.3, pp. 34-54, 2025. DOI:10.5815/ijigsp.2025.03.03
Reference
[1]Khurana, D., Koli, A., Khatter, K., & Singh, S. (2023). Natural language processing: State of the art, current trends and challenges. Multimedia tools and applications, 82(3), 3713-3744.
[2]Fanni, S. C., Febi, M., Aghakhanyan, G., & Neri, E. (2023). Natural language processing. In Introduction to Artificial Intelligence (pp. 87-99). Cham: Springer International Publishing.
[3]Ding, Y., Ma, J., & Luo, X. (2022). Applications of natural language processing in construction. Automation in Construction, 136, 104169.
[4]Sun, T. X., Liu, X. Y., Qiu, X. P., & Huang, X. J. (2022). Paradigm shift in natural language processing. Machine Intelligence Research, 19(3), 169-183.
[5]Treviso, M., Lee, J. U., Ji, T., Aken, B. V., Cao, Q., Ciosici, M. R., ... & Schwartz, R. (2023). Efficient methods for natural language processing: A survey. Transactions of the Association for Computational Linguistics, 11, 826-860.
[6]Wu, H., Wang, M., Wu, J., Francis, F., Chang, Y. H., Shavick, A., ... & Dobson, R. J. (2022). A survey on clinical natural language processing in the United Kingdom from 2007 to 2022. NPJ digital medicine, 5(1), 186.
[7]Wang, Y., Zhu, J., Wang, Z., BAI, F., & Gong, J. (2022). Review of applications of natural language processing in text sentiment analysis. Journal of Computer Applications, 42(4), 1011.
[8]William, P., Shrivastava, A., Chauhan, P. S., Raja, M., Ojha, S. B., & Kumar, K. (2023). Natural Language processing implementation for sentiment analysis on tweets. In Mobile Radio Communications and 5G Networks: Proceedings of Third MRCN 2022 (pp. 317-327). Singapore: Springer Nature Singapore.
[9]Mishra, S., Choubey, S., Choubey, A., Yogeesh, N., Rao, J. D. P., & William, P. (2022, December). Data extraction approach using natural language processing for sentiment analysis. In 2022 International Conference on Automation, Computing and Renewable Systems (ICACRS) (pp. 970-972). IEEE.
[10]Tunca, S., Sezen, B., & Wilk, V. (2023). An exploratory content and sentiment analysis of the guardian metaverse articles using leximancer and natural language processing. Journal of Big Data, 10(1), 82.
[11]Naithani, K., & Raiwani, Y. P. (2023). Realization of natural language processing and machine learning approaches for text‐based sentiment analysis. Expert Systems, 40(5), e13114.
[12]Balli, C., Guzel, M. S., Bostanci, E., & Mishra, A. (2022). Sentimental analysis of Twitter users from Turkish content with natural language processing. Computational Intelligence and Neuroscience, 2022.
[13]Olabanjo, O., Wusu, A., Asokere, M., Padonu, R., Olabanjo, O., Ojo, O., ... & Aribisala, B. (2022). From Twitter to Aso-Rock: A natural language processing spotlight for understanding Nigeria 2023 presidential election.
[14]Sv, P., & Ittamalla, R. (2022). What concerns the general public the most about monkeypox virus?–A text analytics study based on Natural Language Processing (NLP). Travel Medicine and Infectious Disease.
[15]Parks, L., & Peters, W. (2023). Natural language processing in mixed-methods text analysis: A workflow approach. International Journal of Social Research Methodology, 26(4), 377-389.
[16]Hall, K., Chang, V., & Jayne, C. (2022). A review on Natural Language Processing Models for COVID-19 research. Healthcare Analytics, 100078.
[17]Sv, P., Lorenz, J. M., Ittamalla, R., Dhama, K., Chakraborty, C., Kumar, D. V. S., & Mohan, T. (2022). Twitter-based sentiment analysis and topic modeling of social media posts using natural language processing, to understand people’s perspectives regarding COVID-19 booster vaccine shots in India: Crucial to expanding vaccination coverage. Vaccines, 10(11), 1929.
[18]Omuya, E. O., Okeyo, G., & Kimwele, M. (2023). Sentiment analysis on social media tweets using dimensionality reduction and natural language processing. Engineering Reports, 5(3), e12579.
[19]Bari, A., Heymann, M., Cohen, R. J., Zhao, R., Szabo, L., Apas Vasandani, S., ... & Coffee, M. (2022). Exploring coronavirus disease 2019 vaccine hesitancy on Twitter using sentiment analysis and natural language processing algorithms. Clinical Infectious Diseases, 74(Supplement_3), e4-e9.
[20]Sun, T. X., Liu, X. Y., Qiu, X. P., & Huang, X. J. (2022). Paradigm shift in natural language processing. Machine Intelligence Research, 19(3), 169-183.
[21]Shaik, T., Tao, X., Li, Y., Dann, C., McDonald, J., Redmond, P., & Galligan, L. (2022). A review of the trends and challenges in adopting natural language processing methods for education feedback analysis. IEEE Access, 10, 56720-56739.
[22]Fanni, S. C., Febi, M., Aghakhanyan, G., & Neri, E. (2023). Natural language processing. In Introduction to Artificial Intelligence (pp. 87-99). Cham: Springer International Publishing.
[23]Das, J. K., Das, A., & Rosak-Szyrocka, J. (2022, September). A Hybrid Deep Learning Technique for Sentiment Analysis in E-Learning Platform with Natural Language Processing. In 2022 International Conference on Software, Telecommunications and Computer Networks (SoftCOM) (pp. 1-7). IEEE.
[24]Olabanjo, O., Wusu, A., Afisi, O., Asokere, M., Padonu, R., Olabanjo, O., ... & Mazzara, M. (2023). From Twitter to Aso-Rock: A sentiment analysis framework for understanding Nigeria 2023 presidential election. Heliyon, 9(5).
[25]Sait, A. R. W., & Ishak, M. K. (2023). Deep learning with natural language processing enabled sentimental analysis on sarcasm classification. Comput. Syst. Sci. Eng, 44(3), 2553-2567.
[26]Kanakaraddi, S. G., Chikaraddi, A. K., Aivalli, N., Maniyar, J., & Singh, N. (2022, March). Sentiment Analysis of Covid-19 Tweets Using Machine Learning and Natural Language Processing. In Proceedings of Third International Conference on Intelligent Computing, Information and Control Systems: ICICCS 2021 (pp. 367-379). Singapore: Springer Nature Singapore.
[27]Anoop, V. S., & Sreelakshmi, S. (2023). Public discourse and sentiment during Mpox outbreak: an analysis using natural language processing. Public Health, 218, 114-120.
[28]Prajapati, Y., Khande, R., & Parasar, A. (2022, September). Sentiment Analysis of Emotion Detection Using Natural Language Processing. In Proceedings of International Conference on Communication and Computational Technologies: ICCCT 2022 (pp. 223-237). Singapore: Springer Nature Singapore.
[29]Sun, M. (2022). Natural Language Processing for Health System Messages: Deep Transfer Learning Approach to Aspect-Based Sentiment Analysis of COVID-19 Content (Doctoral dissertation, Harvard University).
[30]Nijhawan, T., Attigeri, G., & Ananthakrishna, T. (2022). Stress detection using natural language processing and machine learning over social interactions. Journal of Big Data, 9(1), 1-24.
[31]Jayasudha, J., & Thilagu, M. (2022, December). A Survey on Sentimental Analysis of Student Reviews Using Natural Language Processing (NLP) and Text Mining. In International Conference on Innovations in Intelligent Computing and Communications (pp. 365-378). Cham: Springer International Publishing.
[32]Ayaz, T. B., Uslu, M. S., Ağcabay, İ., Ahmad, F., Korkmaz, O. F., Küreksiz, M., ... & Akbulut, F. P. (2022, September). Global impact of the pandemic on education: a study of natural language processing. In 2022 Innovations in Intelligent Systems and Applications Conference (ASYU) (pp. 1-4). IEEE.
[33]Hasan, M., Islam, L., Jahan, I., Meem, S. M., & Rahman, R. M. (2023). Natural Language Processing and Sentiment Analysis on Bangla Social Media Comments on Russia–Ukraine War Using Transformers. Vietnam Journal of Computer Science, 1-28.
[34]Niazi, F., Elkaim, L. M., Khomami, N. M. Z., Levett, J. J., Weil, A. G., Hodaie, M., & Alotaibi, N. M. (2023). Microvascular decompression and trigeminal neuralgia: patient sentiment analysis using natural language processing. World Neurosurgery.
[35]Hiremath, B. N., & Patil, M. M. (2022). Enhancing optimized personalized therapy in clinical decision support system using natural language processing. Journal of King Saud University-Computer and Information Sciences, 34(6), 2840-2848.
[36]Gunasekaran, K. P. (2023). Exploring Sentiment Analysis Techniques in Natural Language Processing: A Comprehensive Review. arXiv preprint arXiv:2305.14842.
[37]Hasikin, K., Lai, K. W., Satapathy, S. C., Sabanci, K., & Aslan, M. F. (2023). Emerging applications of text analytics and natural language processing in healthcare. Frontiers in Digital Health, 5, 1227948.
[38]Chen, Q., Huang, G., & Wang, Y. (2022). The weighted cross-modal attention mechanism with sentiment prediction auxiliary task for multimodal sentiment analysis. IEEE/ACM Transactions on Audio, Speech, and Language Processing, 30, 2689-2695.
[39]Ghosh, S., Ekbal, A., & Bhattacharyya, P. (2023). Natural language processing and sentiment analysis: perspectives from computational intelligence. In Computational Intelligence Applications for Text and Sentiment Data Analysis (pp. 17-47). Academic Press.
[40]Tran, T. K., Dinh, H. M., & Phan, T. T. (2022). Building an enhanced sentiment classification framework based on natural language processing. Journal of Intelligent & Fuzzy Systems, 43(2), 1771-1777.
[41]Tran, D. D., Nguyen, T. T. S., & Dao, T. H. C. (2022). Sentiment analysis of movie reviews using machine learning techniques. In Proceedings of Sixth International Congress on Information and Communication Technology: ICICT 2021, London, Volume 1 (pp. 361-369). Springer Singapore.