Social Media Content Analyzer

Authors

  • Olubukola D. Adekola, Oluwasomidotun G. Kayode-Afolayan, Oluwatosin Fabiyi, Stephen O. Maitanmi, Wumi Ajayi, Folasade Y. Ayankoya, Jumoke C. Daramola, Michael Agbaje8, Akintoye Onamade, Yaw A. Mensah, Adedoyin S. Adebanjo, Afolarin Amusa

Keywords:

Social media, Content Analyzer, Natural Language Processing, Sentiment Analysis, Artificial Intelligence

Abstract

In an era dominated by online interactions, the potential of social media as a communication medium is immense. As a pivotal channel for disseminating information across various sectors, social media has redefined connectivity, sharing, and communication. However, a significant gap exists in addressing student complaints and opinions, often leading to dissatisfaction and hindrance in their educational experience. This research aims to bridge this gap by developing the Social Media Content Analyzer System. Leveraging Natural Language Processing (NLP) techniques, the system is designed to extract, analyze, and interpret textual information from social media platforms, providing actionable insights to prioritize and enhance students’ lifestyle and learning conditions. The implementation of this system promises a transformative impact on the educational sector. By automating the process of monitoring and addressing student complaints, it ensures a more responsive and conducive learning environment, ultimately contributing to an improved student experience as well as a solution driven institution.

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References

G. Kumari, and A. M. Sowjanya, “An integrated single framework for text, image and voice for sentiment mining of social media posts.” Revue d'Intelligence Artificielle, Vol. 36, No. 3, pp. 381-386, 2022, https://doi.org/10.18280/ria.360305

K. L. Tan, C. P. Lee and K. M. Lim, “A Survey of Sentiment Analysis: Approaches, Datasets, and Future Research,” Applied Sciences, vol. 13, no. 7, 4550, 2023, DOI: https://doi.org/10.3390/app13074550

B. Liu, and L. Zhang, “A survey of opinion mining and sentiment analysis” In C. Aggarwal & C. Zhai (Eds.), Mining Text Data (pp. 415–463). Springer, 2012, https://doi.org/10.1007/978-1-4614-3223-4_13

Margarita Rodríguez-Ibánez, Antonio Casánez-Ventura, Félix Castejón-Mateos and Pedro-Manuel Cuenca-Jiménez, "A review on sentiment analysis from social media platforms," Expert Systems with Applications, ISSN 0957-4174, vol. 223, 119862, 2023, DOI: https://doi.org/10.1016/j.eswa.2023.119862.

M. El-Badaoui, N. Gherabi, and F. Quanouni, “TED talks comments sentiment classification using machine learning algorithms,” Revue d'Intelligence Artificielle, vol. 38, No. 3, pp. 885-892, 2024, https://doi.org/10.18280/ria.380315

L. Wade, “How Social Media is Reshaping Today’s Education System. Georgetown University, School of Continuing Studies,” 2024, Available: https://csic.georgetown.edu/magazine/social-media-reshaping-todays-education-system/

J. E. Barreto, and C. L. Whitehair, “Social media and web presence for patients and professionals: evolving trends and implications for practice,” PM&R, pp. 98-105, Vol. 9, No. 5, 2017, DOI: https://doi.org/10.1016/j.pmrj.2017.02.012

M. V. Mäntylä, D. Graziotin, and M. Kuutila, "The evolution of sentiment analysis: A review of research topics, venues, and top cited papers," Computer Science Review, pp. 16-32, vol. 27, Feb. 2018, DOI: 10.1016/j.cosrev.2017.10.002.

B. Pang, and L. Lee, “Opinion Mining and Sentiment Analysis. Foundations and Trends in Information Retrieval,” pp. 1-135, vol. 2, 6th July 2008, DOI: 10.1561/1500000011.

I. H. Sarker, “Machine Learning: Algorithms, Real-World Applications and Research Directions,” SN COMPUT. SCI. 2, no. 160, 2021, https://doi.org/10.1007/s42979-021-00592-x

X. Liu, C. A. Burns, and H. Yingjian, “An Investigation of Brand-Related User-Generated Content on Twitter.” Journal of Advertising, pp. 236–47, vol, 46, no. 2, 2017, Available: https://www.jstor.org/stable/48542203.

C. Qian, N. Mathur, N. H. Zakaria, R. Arora, V. Gupta et. al., “Understanding public opinions on social media for financial sentiment analysis using AI-based techniques. Information Processing & Management, ISSN 0306-4573, vol. 59, no. 6, 103098, 2022, DOI: https://doi.org/10.1016/j.ipm.2022.103098.

N. Sande, I. Adeniyi, and A. Akinkunmi, “Social Media Sentiment Analysis: A Comprehensive Analysis,” Department of Statistics, Zicolin Power Institute, Nigeria, 2024, DOI: 10.13140/RG.2.2.31094.37441.

T. Praveenkumar, A. Manorselvi, and K. Soundarapandiyan, "Exploring the students feelings and emotion towards online teaching: Sentimental analysis approach," in Proceedings Int. Working Conf. Transfer Diffus. IT (TDIT), December 2020, Tiruchirappalli, India, DOI: 10.1007/978-3-030-64849-7_13, pp. 137-146.

J. Zhang, “Research on sentiment analysis and satisfaction evaluation of online teaching in universities during epidemic prevention,” Frontiers in Psychology, vol. 12, Article 738776, 2021, https://doi.org/10.3389/fpsyg.2021.738776

X. Zhou, X. Liu, and Y. Zhang, "Deep Learning for Sentiment Analysis on Social Media: A Comparative Study," Artificial Intelligence Review, pp. 1-24, vol. 53, no. 1, Jan. 2023, DOI: 10.1007/s10462-019-09715-2.

C. Grimalt-Álvaro, and M. Usart, "Sentiment analysis for formative assessment in higher education: A systematic literature review," Journal of Computing in Higher Education, 2023. [Online]. Available: https://doi.org/10.1007/s12528-023-09370-5.

C. G. Maurya, and S. K. Jha, "Sentiment Analysis: A Hybrid Approach on Twitter Data", Procedia Computer Science, ISSN 1877-0509, pp. 990-999, vol. 235, 2024, DOI: https://doi.org/10.1016/j.procs.2024.04.094.

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Published

06.08.2024

How to Cite

Olubukola D. Adekola. (2024). Social Media Content Analyzer. International Journal of Intelligent Systems and Applications in Engineering, 12(23s), 591–599. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/6925

Issue

Vol. 12 No. 23s (2024)

Section

Research Article

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