Smart Learning Paths: Enhancing Education with Machine Learning Sentiment Analysis
Keywords:
Academic Courses, Machine learning, Sentimental AnalysisAbstract
The post-COVID surge in E-learning led to the need for a robust recommendation system due to the overwhelming number of online courses. Our proposed solution integrates Smart learning paths with advanced web scraping, machine learning, and sentiment analysis to extract comprehensive information from diverse platforms. Smart Learning paths adapts to the dynamic E-learning landscape, offering nuanced insights through quantitative metrics and qualitative sentiment analysis. Rigorous real-world experiments validate its effectiveness, making it a beacon of innovation in reshaping how users navigate and select personalized pathways in online education. The integration of web scraping, machine learning, and sentiment analysis ensures adaptability to evolving needs, transcending traditional metrics and prioritizing user experience.
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P. M. Moreno-Marcos, C. Alario-Hoyos, P. J. Muñoz-Merino, I. Estévez-Ayres and C. D. Kloos, "Sentiment analysis in MOOCs: A case study," 2018 IEEE Global Engineering Education Conference (EDUCON), Santa Cruz de Tenerife, Spain, 2018, pp. 1489-1496, doi: 10.1109/EDUCON.2018.8363409.
Z. Kechaou, M. Ben Ammar and A. M. Alimi, "Improving e-learning with sentiment analysis of users' opinions," 2011 IEEE Global Engineering Education Conference (EDUCON), Amman, Jordan, 2011, pp. 1032-1038, doi: 10.1109/EDUCON.2011.5773275.
H. T. Phan, V. C. Tran, N. T. Nguyen and D. Hwang, "Improving the Performance of Sentiment Analysis of Tweets Containing Fuzzy Sentiment Using the Feature Ensemble Model," in IEEE Access, vol. 8, pp. 14630-14641, 2020, doi: 10.1109/ACCESS.2019.2963702.
X. Zhou, X. Tao, J. Yong and Z. Yang, "Sentiment analysis on tweets for social events," Proceedings of the 2013 IEEE 17th International Conference on Computer Supported Cooperative Work in Design (CSCWD), Whistler, BC, Canada, 2013, pp. 557-562, doi: 10.1109/CSCWD.2013.6581022.
D. Tang, F. Wei, B. Qin, N. Yang, T. Liu and M. Zhou, "Sentiment Embeddings with Applications to Sentiment Analysis," in IEEE Transactions on Knowledge and Data Engineering, vol. 28, no. 2, pp. 496-509, 1 Feb. 2016, doi: 10.1109/TKDE.2015.2489653
L. Stappen, A. Baird, E. Cambria and B. W. Schuller, "Sentiment Analysis and Topic Recognition in Video Transcriptions," in IEEE Intelligent Systems, vol. 36, no. 2, pp. 88-95, 1 March-April 2021, doi: 10.1109/MIS.2021.3062200.
Z. Jianqiang, G. Xiaolin and Z. Xuejun, "Deep Convolution Neural Networks for Twitter Sentiment Analysis," in IEEE Access, vol. 6, pp. 23253-23260, 2018, doi: 10.1109/ACCESS.2017.2776930.
M. Rathi, A. Malik, D. Varshney, R. Sharma and S. Mendiratta, "Sentiment Analysis of Tweets Using Machine Learning Approach," 2018 Eleventh International Conference on Contemporary Computing (IC3), Noida, India, 2018, pp. 1-3, doi: 10.1109/IC3.2018.8530517.
R. B. Shamantha, S. M. Shetty and P. Rai, "Sentiment Analysis Using Machine Learning Classifiers: Evaluation of Performance," 2019 IEEE 4th International Conference on Computer and Communication Systems (ICCCS), Singapore, 2019, pp. 21-25, doi: 10.1109/CCOMS.2019.8821650.
C. Wang, Z. Xiao, Y. Liu, Y. Xu, A. Zhou and K. Zhang, "SentiView: Sentiment Analysis and Visualization for Internet Popular Topics," in IEEE Transactions on Human-Machine Systems, vol. 43, no. 6, pp. 620-630, Nov. 2013, doi: 10.1109/THMS.2013.2285047.
Liu, B., 2010. Sentiment analysis: A multi-faceted problem. IEEE intelligent systems, 25(3), pp.76-80.
G. Xu, Y. Meng, X. Qiu, Z. Yu and X. Wu, "Sentiment Analysis of Comment Texts Based on BiLSTM," in IEEE Access, vol. 7, pp. 51522-51532, 2019, doi: 10.1109/ACCESS.2019.2909919.
M. Abirami and V. Gayathri, "A survey on sentiment analysis methods and approach," 2016 Eighth International Conference on Advanced Computing (ICoAC), Chennai, India, 2017, pp. 72-76, doi: 10.1109/ICoAC.2017.7951748.
D. Li and J. Qian, "Text sentiment analysis based on long short-term memory," 2016 First IEEE International Conference on Computer Communication and the Internet (ICCCI), Wuhan, China, 2016, pp. 471-475, doi: 10.1109/CCI.2016.7778967.
S. Vanaja and M. Belwal, "Aspect-Level Sentiment Analysis on E-Commerce Data," 2018 International Conference on Inventive Research in Computing Applications (ICIRCA), Coimbatore, India, 2018, pp. 1275-1279, doi: 10.1109/ICIRCA.2018.8597286.
L. Khan, A. Amjad, N. Ashraf, H. -T. Chang and A. Gelbukh, "Urdu Sentiment Analysis With Deep Learning Methods," in IEEE Access, vol. 9, pp. 97803-97812, 2021, doi: 10.1109/ACCESS.2021.3093078
M. Yasen and S. Tedmori, "Movies Reviews Sentiment Analysis and Classification," 2019 IEEE Jordan International Joint Conference on Electrical Engineering and Information Technology (JEEIT), Amman, Jordan, 2019, pp. 860-865, doi: 10.1109/JEEIT.2019.8717422.
K. Chakraborty, S. Bhattacharyya and R. Bag, "A Survey of Sentiment Analysis from Social Media Data," in IEEE Transactions on Computational Social Systems, vol. 7, no. 2, pp. 450-464, April 2020, doi: 10.1109/TCSS.2019.2956957.
J. Shen, C. Wang, C. -F. Lai, A. Wang and H. -C. Chao, "Direction Density-Based Secure Routing Protocol for Healthcare Data in Incompletely Predictable Networks," in IEEE Access, vol. 4, pp. 9163-9173, 2016, doi: 10.1109/ACCESS.2016.2637887.
Z. Li, R. Li and G. Jin, "Sentiment Analysis of Danmaku Videos Based on Naïve Bayes and Sentiment Dictionary," in IEEE Access, vol. 8, pp. 75073-75084, 2020, doi: 10.1109/ACCESS.2020.2986582.
Osmanoğlu, U. Ö., Atak, O. N., Çağlar, K., Kayhan, H., et al. (2020). Sentiment Analysis for Distance Education Course Materials: A Machine Learning Approach. Journal of Educational Technology and Online Learning, 3(1), 31-48. https://doi.org/10.31681/jetol.663733.
Rao, K. Yogeswara, G. S. N. Murthy, and S. Adinarayana. "Product recommendation system from users reviews using sentiment analysis." International Journal of Computer Applications 975 (2017): 8887.
Alshamsi, A., Bayari, R., & Salloum, S. (in press). Sentiment analysis in English texts. Advances in science, technology and engineering systems journal, 5(6), 1683-1689. https://doi.org/10.25046/aj0506200
Raza, Hassan, et al. "Scientific text sentiment analysis using machine learning techniques." International Journal of Advanced Computer Science and Applications 10.12 (2019): 157-165.
Alshamsi, A., Bayari, R., & Salloum, S. (in press). Sentiment analysis in English texts. Advances in science, technology and engineering systems journal, 5(6), 1683-1689. https://doi.org/10.25046/aj0506200
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