Volume 2 , Issue 2 , PP: 01-13, 2024 | Cite this article as | XML | Html | PDF | Full Length Article
Manish Kumar Singla 1 * , Amel Ali Alhussan 2
Doi: https://doi.org/10.54216/MOR.020201
Social media sentiment analysis has benefited from the miracle of artificial intelligence (AI), mainly how it can handle large, conflated data sets and distill valuable insights. In this review, the authors consider the positive impact of AI in business, health care, politics, and social justice, including marketing, mental health screening, misinformation, and multilingualism. Using ML and NLP, artificial intelligence technologies empower real-time analysis of the social trends and behaviors that affect decision-making and social interactions. However, many challenges are still reflected in data imbalance, ethical concerns relating to privacy and consent, and difficulties in processing dynamic content and several modalities, languages, and emotional states. Such limitations call for interdisciplinary collaboration and comprehensible ethical guidelines, including the FAIR principles for bettering data stewardship and ensuring no biases in AI systems. When developed as scalable, context-aware, and equitable systems, opinion mining may help solve social dilemmas and build an inclusive digital environment. Based on current trends, challenges, and suggested future directions, this review underlines the need for ethical, interdisciplinary, and culturally sensitive approaches to unlock the proper potential of AI in SA and social media sentiments.
Artificial Intelligence , Sentiment Analysis , Social Media , Machine Learning , Natural Language Processing , Ethical AI
[1] H. Küçükali and M. S. Erdoğan, “AI for tobacco control: identifying tobacco-promoting social media content using large language models,” Nicotine & Tobacco Research, Nov. 2024, doi: 10.1093/NTR/NTAE276.
[2] M. S. Hossain, M. R. Islam, Dr. B. R. Riskhan, M. M. H. HASAN, and R. I. ISLAM, “Political sentiment analysis using natural language processing on social media,” International Journal of Applied Methods in Electronics and Computers, vol. 12, no. 4, pp. 81–89, Dec. 2024, doi: 10.58190/IJAMEC.2024.108.
[3] S. Ahmed, S. Rakin, M. W. I. Waliur, N. B. Islam, B. Hossain, and Md. M. Akbar, “Depression detection from Social Media Bangla Text Using Recurrent Neural Networks,” Dec. 2024, Accessed: Dec. 13, 2024. [Online]. Available: https://arxiv.org/abs/2412.05861v1
[4] T. Mikolov, K. Chen, G. Corrado, and J. Dean, “Efficient estimation of word representations in vector space,” 1st International Conference on Learning Representations, ICLR 2013 - Workshop Track Proceedings, 2013.
[5] H. Küçükali and M. Sarper Erdoğan, “AI for Tobacco Control: Identifying Tobacco-promoting Social Media Content Using Large Language Models,” Nicotine Tob Res, Nov. 2024, doi: 10.1093/NTR/NTAE276.
[6] S. Ahmed, S. Rakin, M. W. I. Waliur, N. B. Islam, B. Hossain, and Md. M. Akbar, “Depression detection from Social Media Bangla Text Using Recurrent Neural Networks,” Dec. 2024, Accessed: Dec. 13, 2024. [Online]. Available: https://arxiv.org/abs/2412.05861v1
[7] T. Joseph, “Natural Language Processing (NLP) for Sentiment Analysis in Social Media,” International Journal of Computing and Engineering, vol. 6, no. 2, pp. 35–48, Jul. 2024, doi: 10.47941/IJCE.2135.
[8] H. Chen et al., “A Self-Learning Multimodal Approach for Fake News Detection,” Dec. 2024, Accessed: Dec. 13, 2024. [Online]. Available: https://arxiv.org/abs/2412.05843v1
[9] H. Küçükali and M. Sarper Erdoğan, “AI for Tobacco Control: Identifying Tobacco-promoting Social Media Content Using Large Language Models,” Nicotine Tob Res, Nov. 2024, doi: 10.1093/NTR/NTAE276.
[10] T. Sun et al., “Title: Co-designing AI-generated vaping awareness materials with adolescents and young adults: A qualitative study.”
[11] E. Aytaç, N. K. Khanzada, Y. Ibrahim, M. Khayet, and N. Hilal, “Reverse Osmosis Membrane Engineering: Multidirectional Analysis Using Bibliometric, Machine Learning, Data, and Text Mining Approaches,” Membranes 2024, Vol. 14, Page 259, vol. 14, no. 12, p. 259, Dec. 2024, doi: 10.3390/MEMBRANES14120259.
[12] M. S. Hossain, M. R. Islam, Dr. B. R. Riskhan, M. M. H. HASAN, and R. I. ISLAM, “Political sentiment analysis using natural language processing on social media,” International Journal of Applied Methods in Electronics and Computers, vol. 12, no. 4, pp. 81–89, Dec. 2024, doi: 10.58190/IJAMEC.2024.108.
[13] S. Ahmed, S. Rakin, M. W. I. Waliur, N. B. Islam, B. Hossain, and Md. M. Akbar, “Depression detection from Social Media Bangla Text Using Recurrent Neural Networks,” Dec. 2024, Accessed: Dec. 12, 2024. [Online]. Available: https://arxiv.org/abs/2412.05861v1
[14] A. Lukose, R. S. Cleetus, H. Divya, T. M. Saravanakumar, and J. Jose, “Exploring the Intersection of Brands and Linguistics: A Comprehensive Bibliometric Study,” International Review of Management and Marketing, vol. 15, no. 1, pp. 257–271, Dec. 2025, doi: 10.32479/IRMM.17538.
[15] A. Lukose, R. S. Cleetus, H. Divya, T. M. Saravanakumar, and J. Jose, “Exploring the Intersection of Brands and Linguistics: A Comprehensive Bibliometric Study,” International Review of Management and Marketing, vol. 15, no. 1, pp. 257–271, Dec. 2025, doi: 10.32479/IRMM.17538.
[16] M. F. Fachrudin, C. V. Angkoso, and D. A. Fatah, “Analisis Sentimen Pada Sosial Media Twitter Terhadap Kualitas Jaringan Internet Telkomsel Menggunakan Ensemble K-Nearest Neighbour -Support Vector Machine,” Jurnal Teknologi Informasi dan Ilmu Komputer, vol. 11, no. 6, pp. 1253–1264, Dec. 2024, doi: 10.25126/JTIIK.1168713.
[17] G. Wijaya, D. Irawan, Z. Arifin, H. Oktavianto, M. Rahman, and G. Abdurrahman, “STUDI KLASIFIKASI TOPIK BERITA DENGAN ALGORITMA MACHINE LEARNING,” J-ENSITEC, vol. 11, no. 01, pp. 10202–10206, Dec. 2024, doi: 10.31949/JENSITEC.V11I01.12037.
[18] J. Wandeto, I. Musyoka, and B. Kituku, “Integrating BERT and RESNET50V2 for Multimodal Cyberbullying Detection,” 2024 Sixth International Conference on Intelligent Computing in Data Sciences (ICDS), pp. 1–6, Oct. 2024, doi: 10.1109/ICDS62089.2024.10756337.
[19] S. Pourroostaei Ardakani et al., “Identifying crowdfunding storytellers who deliver successful projects: a machine learning approach,” The Journal of Supercomputing 2024 81:1, vol. 81, no. 1, pp. 1–30, Dec. 2024, doi: 10.1007/S11227-024-06785-4.
[20] A. Ghafouri, H. Naderi, and M. Firouzmandi, “PinLID: a dataset for Pinglish language identification based on code-mixing sentence on unstructured resources,” Language Resources and Evaluation 2024, pp. 1–27, Dec. 2024, doi 10.1007/S10579-024-09783-3.
[21] S. Cozzini and M. de Luca, “Living in Digital Ecosystems: Are We Aware of This?,” pp. 113–132, 2024, doi: 10.1007/978-3-031-76961-0_6.
[22] Z. N. Vasha, B. Sharma, I. J. Esha, J. Al Nahian, and J. A. Polin, “Depression detection in social media comments data using machine learning algorithms,” Bulletin of Electrical Engineering and Informatics, vol. 12, no. 2, pp. 987–996, Apr. 2023, doi: 10.11591/EEI.V12I2.4182.
[23] M. S. Hossain, M. R. Islam, Dr. B. R. Riskhan, M. M. H. HASAN, and R. I. ISLAM, “Political sentiment analysis using natural language processing on social media,” International Journal of Applied Methods in Electronics and Computers, vol. 12, no. 4, pp. 81–89, Dec. 2024, doi: 10.58190/IJAMEC.2024.108.
[24] H. Chen et al., “A Self-Learning Multimodal Approach for Fake News Detection,” Dec. 2024, Accessed: Dec. 12, 2024. [Online]. Available: https://arxiv.org/abs/2412.05843v1
[25] X. Shen, M. Huang, Z. Hu, S. Cai, and T. Zhou, “Multimodal Fake News Detection with Contrastive Learning and Optimal Transport,” Front Comput Sci, vol. 6, p. 1473457, Nov. 2024, doi: 10.3389/FCOMP.2024.1473457/BIBTEX.
[26] X. Shen, M. Huang, Z. Hu, S. Cai, and T. Zhou, “Multimodal Fake News Detection with Contrastive Learning and Optimal Transport,” Front Comput Sci, vol. 6, p. 1473457, Nov. 2024, doi: 10.3389/FCOMP.2024.1473457/BIBTEX.
[27] H. Küçükali and M. S. Erdoğan, “Identification and classification of tobacco-promoting social media content at scale using deep learning: a mixed-methods study,” Popul Med, vol. 5, no. Supplement, Apr. 2023, doi: 10.18332/POPMED/164106.
[28] M. S. Hossain, M. R. Islam, Dr. B. R. Riskhan, M. M. H. HASAN, and R. I. ISLAM, “Political sentiment analysis using natural language processing on social media,” International Journal of Applied Methods in Electronics and Computers, vol. 12, no. 4, pp. 81–89, Dec. 2024, doi: 10.58190/IJAMEC.2024.108.
[29] J. Wandeto, I. Musyoka, and B. Kituku, “Integrating BERT and RESNET50V2 for Multimodal Cyberbullying Detection,” 2024 Sixth International Conference on Intelligent Computing in Data Sciences (ICDS), pp. 1–6, Oct. 2024, doi: 10.1109/ICDS62089.2024.10756337.
[30] S. Ahmed, S. Rakin, M. W. I. Waliur, N. B. Islam, B. Hossain, and Md. M. Akbar, “Depression detection from Social Media Bangla Text Using Recurrent Neural Networks,” Dec. 2024, Accessed: Dec. 13, 2024. [Online]. Available: https://arxiv.org/abs/2412.05861v1
[31] A. Ghafouri, H. Naderi, and M. Firouzmandi, “PinLID: a dataset for Pinglish language identification based on code-mixing sentence on unstructured resources,” Lang Resour Eval, Dec. 2024, doi 10.1007/S10579-024-09783-3.
[32] S. Pourroostaei Ardakani et al., “Identifying crowdfunding storytellers who deliver successful projects: a machine learning approach,” The Journal of Supercomputing 2024 81:1, vol. 81, no. 1, pp. 1–30, Dec. 2024, doi: 10.1007/S11227-024-06785-4.
[33] S. Cozzini and M. de Luca, “Living in Digital Ecosystems: Are We Aware of This?,” pp. 113–132, 2024, doi: 10.1007/978-3-031-76961-0_6.
[34] Z. N. Vasha, B. Sharma, I. J. Esha, J. Al Nahian, and J. A. Polin, “Depression detection in social media comments data using machine learning algorithms,” Bulletin of Electrical Engineering and Informatics, vol. 12, no. 2, pp. 987–996, Apr. 2023, doi: 10.11591/EEI.V12I2.4182.
[35] H. Chen et al., “A Self-Learning Multimodal Approach for Fake News Detection,” Dec. 2024, Accessed: Dec. 13, 2024. [Online]. Available: https://arxiv.org/abs/2412.05843v1