eISSN: 3087-4262 editor@aimjournals.com
All Journals
Submit your article

International Journal of Intelligent Data and Machine Learning

Open Access Peer Review International
Open Access

Sentiment Analysis of Social Network Comments for Identifying Opinion Leaders Using Machine Learning

DOI https://doi.org/10.55640/ijidml-v03i06-04
PDF
Ripunjay Kumar 1 ,
4 Research Scholar Jabalpur, India

Abstract

The rate of social media growth is the greatest determinant in the spread of information. In order to understand and change opinion of the majority, it is necessary to find Key Opinion Leaders. In this research, a technique is provided for detecting opinion leaders in social media communications by analyzing their sentiment using Sentiment140. Following a long pre-processing sequence that includes text cleaning, tokenization, stemming, normalization, and Bag-of-Words feature extraction, the class imbalance problem is solved by using the SMOTE approach. The suggested method find geographical patterns and causal linkages in the text by using deep learning models like Bidirectional Long Short-Term Memory (BiLSTM) networks and Convolutional Neural Networks (CNNs). Compared to other machine learning models, such as Naive Bayes, Adaboost, and Logistic Regression, the suggested models performed better in the empirical data. The F1-score (F1), accuracy (ACC), and recall (REC) of these models are all above average, reaching at 98%. The results indicated that the deep learning algorithms can perform very well in the field of identifying opinion leaders based on sentiment analysis.

Keywords

Text Classification, Social Network, Machine Learning, Opinion Leader

References

M. Zhu, X. Lin, T. Lu, and H. Wang, “Identification of Opinion Leaders in Social Networks Based on Sentiment Analysis: Evidence from an Automotive Forum,” vol. 58, no. Msota, pp. 412–416, 2016, doi: 10.2991/msota-16.2016.91.
S. R. El-Beltagy, T. Khalil, A. Halaby, and M. Hammad, “Combining Lexical Features and a Supervised Learning Approach for Arabic Sentiment Analysis,” 2018, pp. 307–319. doi: 10.1007/978-3-319-75487-1_24.
M. N. Paralath, “Bridging Astronomical Imagery and Text with Advanced Multi-Modal AI,” in 2025 IEEE 7th International Conference on Computing, Communication and Automation (ICCCA), Greater Noida, India: IEEE, 2025, pp. 1–8, November. doi: 10.1109/ICCCA66364.2025.11325649.
H. Ombabi, W. Ouarda, and A. M. Alimi, “Deep learning CNN–LSTM framework for Arabic sentiment analysis using textual information shared in social networks,” Soc. Netw. Anal. Min., vol. 10, no. 1, p. 53, Dec. 2020, doi: 10.1007/s13278-020-00668-1.
N. Alapati, R. rao Thallada, and K. Nallabothu, “Risk-Based Governance for Autonomous Decision Systems,” J. Bus. Manag. Stud., vol. 8, no. 6, April, pp. 69–73, 2026, doi: 10.32996/jbms.
Alsayat and N. Elmitwally, “A comprehensive study for Arabic Sentiment Analysis (Challenges and Applications),” Egypt. Informatics J., 2020, doi: 10.1016/j.eij.2019.06.001.
Y. Zahidi, Y. El Younoussi, and Y. Al-Amrani, “A powerful comparison of deep learning frameworks for Arabic sentiment analysis,” Int. J. Electr. Comput. Eng., vol. 11, no. 1, p. 745, Feb. 2021, doi: 10.11591/ijece.v11i1.pp745-752.
Padhy, N. Seshagiri, V. Soni, A. K. Elengovan, G. Babu Thokala, and M. Kumari, “AI-Enabled Autonomous Data Preprocessing: A Scalable Architecture for Intelligent Machine Learning Pipeline Management,” in 2026 6th International Conference on Image Processing and Capsule Networks (ICIPCN), Jan. 2026, pp. 1318–1323. doi: 10.1109/ICIPCN67432.2026.11438503.
Patel, “Hybrid Deep Learning Approaches for Personalized Product Recommendation in Online Retail Systems,” in 2026 6th International Conference on Innovative Research in Applied Science, Engineering and Technology (IRASET), FEZ, Morocco: IEEE, 2026, pp. 1–6, May. doi: 10.1109/IRASET68627.2026.11538894.
R. M. Duwairi and I. Qarqaz, “Arabic Sentiment Analysis Using Supervised Classification,” in 2014 International Conference on Future Internet of Things and Cloud, IEEE, Aug. 2014, pp. 579–583. doi: 10.1109/FiCloud.2014.100.
M. El-Masri, N. Altrabsheh, H. Mansour, and A. Ramsay, “A web-based tool for Arabic sentiment analysis,” Procedia Comput. Sci., vol. 117, pp. 38–45, 2017, doi: 10.1016/j.procs.2017.10.092.
A.-K. Al-Tamimi, A. Shatnawi, and E. Bani-Issa, “Arabic sentiment analysis of YouTube comments,” in 2017 IEEE Jordan Conference on Applied Electrical Engineering and Computing Technologies (AEECT), IEEE, Oct. 2017, pp. 1–6. doi: 10.1109/AEECT.2017.8257766.
Alharbi, M. Kalkatawi, and M. Taileb, “Arabic Sentiment Analysis Using Deep Learning and Ensemble Methods,” Arab. J. Sci. Eng., vol. 46, no. 9, pp. 8913–8923, Sep. 2021, doi: 10.1007/s13369-021-05475-0.
R. Azmeera and J. Hyatt, “Software Errors, Product Functionality, and Organizational Cascades: Evidence from Developer-Lived Experience,” 2026.
Guellil, F. Azouaou, and M. Mendoza, “Arabic sentiment analysis: studies, resources, and tools,” Soc. Netw. Anal. Min., vol. 9, no. 1, p. 56, Dec. 2019, doi: 10.1007/s13278-019-0602-x.
R. M. Duwairi, N. A. Ahmed, and S. Y. Al-Rifai, “Detecting sentiment embedded in Arabic social media – A lexicon-based approach,” J. Intell. Fuzzy Syst., vol. 29, no. 1, pp. 107–117, Sep. 2015, doi: 10.3233/IFS-151574.
Dixit, “Predictive Analytics in Business Intelligence for Sales Forecasting,” Int. J. Adv. Res. Sci. Commun. Technol., vol. 03, no. 01, August, pp. 981–991, 2023, doi: 10.48175/IJARSCT-12750G.
S. Dodda, H. Volikatla, and J. R. Vummadi, “Exploring the Role of AI-Enhanced Chatbots in Automating Recruitment Processes in Human Capital Management Systems,” Int. J. Emerg. Trends Comput. Sci. Inf. Technol., vol. 6, no. 3, July, pp. 28–36, 2025, doi: https://doi.org/10.63282/3050-9246.IJETCSIT-V6I3P104.
Alqahtani, S. B. Khan, J. Alqahtani, S. AlYami, and F. Alfayez, “Sentiment Analysis of Semantically Interoperable Social Media Platforms Using Computational Intelligence Techniques,” Appl. Sci., vol. 13, no. 13, Jun. 2023, doi: 10.3390/app13137599.
S. Borhade, S. Bhoir, A. Tumsare, H. Harkulkar, S. Mhatre, and M. Trivedi, “Reddit-Based Social Media Sentiment and Emotion Analysis for Product Improvement Using Hadoop,” in 2026 International Conference on Communication, Computing and Emerging Technologies (IC3ET), IEEE, Feb. 2026, pp. 816–820. doi: 10.1109/IC3ET64989.2026.11467286.
V. S. Abeynayake, S. A. S. Lorensuhewa, and M. A. L. Kalyani, “Fine-Tuning SinLlama for Sentiment Analysis in Romanized Sinhala-English Code-Mixed Social Media Text,” in 2026 6th International Conference on Advanced Research in Computing (ICARC), IEEE, Feb. 2026, pp. 1–5. doi: 10.1109/ICARC68737.2026.11453630.
X. Zhang, J. Li, and M. Zhao, “The Research on Social Media Sentiment Analysis and Prediction Model Based on Graph Neural Networks,” in 2025 5th International Symposium on Artificial Intelligence and Intelligent Manufacturing (AIIM), IEEE, Sep. 2025, pp. 376–379. doi: 10.1109/AIIM67611.2025.11232724.
Prabha, A. Yadav, A. Yadav, A. Kumar, and M. Tyagi, “Toxic Comments Classification using Machine Learning and Word Embedding Techniques,” in 2025 2nd International Conference on Computational Intelligence, Communication Technology and Networking (CICTN), IEEE, Feb. 2025, pp. 599–604. doi: 10.1109/CICTN64563.2025.10932491.
Singh and S. Kumar, “Stress Detection During Social Interactions with Natural Language Processing and Machine Learning,” in 2024 International Conference on Expert Clouds and Applications (ICOECA), IEEE, Apr. 2024, pp. 297–301. doi: 10.1109/ICOECA62351.2024.00060.
Rai and P. Kumar, “Comparative Analysis of Machine Learning and Deep Learning Techniques in Text Based Emotion Detection,” in 2023 International Conference on Computational Intelligence and Sustainable Engineering Solutions (CISES), IEEE, Apr. 2023, pp. 260–263. doi: 10.1109/CISES58720.2023.10183612.
Singamsetty, “AI-powered Satellite Image Processing for Global Air Traffic Surveillance Techniques Using NCNN–EGSA Optimization Techniques,” in Machine Learning Based Air Traffic Surveillance System Using Image Processing, Emerald Publishing Limited, 2026, pp. 179–197. doi: 10.1108/978-1-80592-062-520251010.
Korat and A. Alimohammad, “Efficient Hardware Implementation of Eigen Solver,” in 2026 IEEE 16th Annual Computing and Communication Workshop and Conference (CCWC), Las Vegas, NV, USA: IEEE, 2026, pp. 1376–1385, January. doi: 10.1109/CCWC67433.2026.11393888.
Sathya, K. Thulasimani, and S. Devasurithi, “A study on twitter sentiment analysis using deep learning,” Int. Res. J. Eng. Technol., pp. 799–804, 2023.
S. Shah, “Twitter Sentiment Analysis Using Recurrent,” Int. J. Sci. Technoledge, vol. 10, no. 10, pp. 5–9, 2022.
Gaye, D. Zhang, and A. Wulamu, “A Tweet Sentiment Classification Approach Using a Hybrid Stacked Ensemble Technique,” Information, vol. 12, no. 9, p. 374, Sep. 2021, doi: 10.3390/info12090374.
P. Durga and T. Sudhakar, “A decision-making framework utilizing machine learning techniques, based on analysing the sentiment of tweets,” Multidiscip. Sci. J., vol. 5, p. 2023ss0102, Aug. 2023, doi: 10.31893/multiscience.2023ss0102.

Similar Articles

41-50 of 53

You may also start an advanced similarity search for this article.