Evaluation of Machine Learning Techniques for Enhancing Scholarship Schemes Using Artificial Emotional Intelligence





Emotion AI, Opinion Mining, SentiwordNet, WordNet, Sentiment Polarity Classification


This paper investigates the sentiment analysis of the” scholarship system” [4], in Odisha, primarily, to identify why some students do not apply for government-sponsored scholarships. Our research focuses on social media platforms, surveys, and machine learning-based analyses to understand the decision-making process and increase awareness about the various scholarship schemes. The goal of our experiment is to determine the efficacy of sentiment analysis in evaluating the effectiveness of different scholarship schemes. A wide variety of techniques based on dictionaries; corpora lexicons are used in different scholarship schemes for sentiment analysis. Our research paper is based on an evaluation process that could have a positive effect on the government by improving scholarship programs and giving financial aid to students from poor families, which would raise the level of education in Odisha. Our research paper concludes with a summary of successful and unsuccessful schemes, as well as their Word frequency counts and Sentiment Polarity scores.


Download data is not yet available.
<br data-mce-bogus="1"> <br data-mce-bogus="1">


S. Behdenna, F. Barigou, and G. Belalem, “Document Level Sentiment Analysis: A survey,” EAI Endorsed Trans. Context. Syst. Appl., 2018, vol. 4, no.13, p 154339, doi: 10.4108/eai.14-3-2018.154339. DOI: https://doi.org/10.4108/eai.14-3-2018.154339

E. Cambria, “An introduction to concept-level sentiment analysis,” Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics), 2013, vol. 8266 LNAI, no. PART 2, pp. 478–483, doi: 10.1007/978-3-642-45111- 941. DOI: https://doi.org/10.1007/978-3-642-45111-9_41

H. Liu, I. Chatterjee, M. Zhou, X. S. Lu, and A. Abusorrah, “Aspect-Based Sentiment Analysis: A Survey of Deep Learning Methods”, IEEE Trans. Comput. Soc. Syst., 2020, vol. 7, no. 6, pp. 1358–1375, doi: 10.1109/TCSS.2020.3033302. DOI: https://doi.org/10.1109/TCSS.2020.3033302

X. Ma, Q. Zhang, Y. Cui, J. Qu, and F. Yue, “Evaluation analysis of university student scholarship,” Proc. Int. Symp. Test Meas., 2009, vol. 2, pp. 152–155, doi: 10.1109/ICTM.2009.5413089. DOI: https://doi.org/10.1109/ICTM.2009.5413089

S. Ranathunga and I. U. Liyanage, “Sentiment Analysis of Sinhala News Comments,” ACM Trans. Asian Low-Resource Lang. Inf. Process., 2021, vol. 20, no. 4, doi: 10.1145/3445035. DOI: https://doi.org/10.1145/3445035

S. Poria, A. Gelbukh, E. Cambria, P. Yang, A. Hussain, and T. Durrani, “Merging SenticNet and WordNet-Affect emotion lists for sentiment analysis,” Int. Conf. Signal Process. Proceedings, ICSP, 2012, vol. 2, pp. 1251–1255, doi: 10.1109/ICoSP.2012.6491803. DOI: https://doi.org/10.1109/ICoSP.2012.6491803

R. Yang, “Unsupervised machine learning for physical concepts”, 2022, pp. 31–34, [Online]. Available: http://arxiv.org/abs/2205.05279.

Ghosh, H., Tusher, M.A., Rahat, I.S., Khasim, S., Mohanty, S.N. (2023). Water Quality Assessment Through Predictive Machine Learning. In: Intelligent Computing and Networking. IC-ICN 2023. Lecture Notes in Networks and Systems, vol 699. Springer, Singapore. https://doi.org/10.1007/978-981-99-3177-4_6 DOI: https://doi.org/10.1007/978-981-99-3177-4_6

Rahat IS, Ghosh H, Shaik K, Khasim S, Rajaram G. Unraveling the Heterogeneity of Lower-Grade Gliomas: Deep Learning-Assisted Flair Segmentation and Genomic Analysis of Brain MR Images. EAI Endorsed Trans Perv Health Tech [Internet]. 2023 Sep. 29 [cited 2023 Oct. 2];9. https://doi.org/10.4108/eetpht.9.4016 DOI: https://doi.org/10.4108/eetpht.9.4016

Ghosh H, Rahat IS, Shaik K, Khasim S, Yesubabu M. Potato Leaf Disease Recognition and Prediction using Convolutional Neural Networks. EAI Endorsed Scal Inf Syst [Internet]. 2023 Sep. 21 https://doi.org/10.4108/eetsis.3937 DOI: https://doi.org/10.4108/eetsis.3937

Mandava, S. R. Vinta, H. Ghosh, and I. S. Rahat, “An All-Inclusive Machine Learning and Deep Learning Method for Forecasting Cardiovascular Disease in Bangladeshi Population”, EAI Endorsed Trans Perv Health Tech, vol. 9, Oct. 2023. https://doi.org/10.4108/eetpht.9.4052 DOI: https://doi.org/10.4108/eetpht.9.4052

Mandava, M.; Vinta, S. R.; Ghosh, H.; Rahat, I. S. Identification and Categorization of Yellow Rust Infection in Wheat through Deep Learning Techniques. EAI Endorsed Trans IoT 2023, 10. https://doi.org/10.4108/eetiot.4603 DOI: https://doi.org/10.4108/eetiot.4603

Khasim, I. S. Rahat, H. Ghosh, K. Shaik, and S. K. Panda, “Using Deep Learning and Machine Learning: Real-Time Discernment and Diagnostics of Rice-Leaf Diseases in Bangladesh”, EAI Endorsed Trans IoT, vol. 10, Dec. 2023 https://doi.org/10.4108/eetiot.4579 DOI: https://doi.org/10.4108/eetiot.4579

Khasim, H. Ghosh, I. S. Rahat, K. Shaik, and M. Yesubabu, “Deciphering Microorganisms through Intelligent Image Recognition: Machine Learning and Deep Learning Approaches, Challenges, and Advancements”, EAI Endorsed Trans IoT, vol. 10, Nov. 2023. https://doi.org/10.4108/eetiot.4484 DOI: https://doi.org/10.4108/eetiot.4484

Mohanty, S.N.; Ghosh, H.; Rahat, I.S.; Reddy, C.V.R. Advanced Deep Learning Models for Corn Leaf Disease Classification: A Field Study in Bangladesh. Eng. Proc. 2023, 59, 69. https://doi.org/10.3390/engproc2023059069 DOI: https://doi.org/10.3390/engproc2023059069

Alenezi, F.; Armghan, A.; Mohanty, S.N.; Jhaveri, R.H.; Tiwari, P. Block-Greedy and CNN Based Underwater Image Dehazing for Novel Depth Estimation and Optimal Ambient Light. Water 2021, 13, 3470. https://doi.org/10.3390/w13233470 DOI: https://doi.org/10.3390/w13233470




How to Cite

P. S. Raju, S. K. Patra, and B. K. Patra, “Evaluation of Machine Learning Techniques for Enhancing Scholarship Schemes Using Artificial Emotional Intelligence”, EAI Endorsed Trans IoT, vol. 10, Mar. 2024.