Application of Machine Learning to Developing an Internet Community Model  

Abstract

This study will assist in determining the types of elements and occasions that influence people's opinions. It aims to implement a social media sentiment Social Media Sentiment Analysis using machine learning techniques. The objectives of this study are as follows: (i) To combine natural language processing, machine learning, and financial modeling to analyze the different impacts of sentiments on social media.  (ii) To implement the developed model in (i) (iii) To evaluate the performance of the developed model in (ii). The method used in this study includes the application downloads of tweets from Tweeter and inserts the data into the MongoDB database. The Natural Language Toolkit corpus called Twitter samples is available for the training dataset, the application extracts features from the training dataset. The insights would help the people for example in the National Security Department and Emergency Response Teams to understand the public emotional behaviors towards certain events and people, take appropriate actions with informed decisions, and perform situational analysis regarding public safety and security. Pymongo retrieved the text driver from the Tweets. The classifier model assigned polarity to each tweet and displayed the Tweet. Data visualization was successful for the retrieved system for Twitter user followers, friends, status counts, and charts to visualize the data. The application displayed exploratory visualization using line charts, bar charts, and tweets on a map using coordinates.

Keywords: Online social media, Sentiment analysis, Supervised learning, Natural language processing, Behavioral analysis  

[1] Y. Acar, G.  Demet, and M. Kaynar, “Data and discourse: an assessment of Taksim urban design competition in terms of populism and participation”, Journal of Urban Design, no. 28, vol. 6, pp 682-698, 2023.

[2] S. Anno, Y. Kimura, and S. Sugita, “Using transformer-based models and social media posts for heat stroke detection”, Scientific Reports, vol. 15, no. 1, pp. 742, 2025.

[3] M. G. Albino, “Opinion-Mining Technique on Generative Artificial Intelligence Topic Using Data Classification Algorithms”.

[4] J. P. Bharadiya, “A review of Bayesian machine learning principles, methods, and applications”, International Journal of Innovative Science and Research Technology, vol. 8, no. 5, pp. 2033-2038, 2023.

[5] S. H. Chowdhury, M. Pathan, K.E. Arefin, D. Mohd, and Noorul Huq, “Prevalence and Effect of Computer Vision Syndrome during COVID-19: among Bangladeshi People”, SAS J Med, vol. 4, pp. 204-211, 2024.

[6] K. Chhutlani, V. Takrani, A. Motwani, T. Harchandani, and S. Sahu, “Sentiment Analysis of OYO Hotel Reviews Using NLP”, In 2023 14th International Conference on Computing Communication and Networking Technologies (ICCCNT), pp. 1-6, IEEE, 2023.

[7] R. K. Godara, A. Mengi, A. Sharma, and S. Sharma, “Detecting Depressive Symptoms on Social Media: A Comprehensive Review of Methodologies and Strategies for Suicide Prevention”, In International Conference on Cognitive Computing and Cyber Physical Systems, pp. 87-100, Singapore: Springer Nature Singapore, 2023.

[8] A. K. Jha, and N. K. Verma, “Social media sustainability communication: an analysis of firm behaviour and stakeholder responses”, Information Systems Frontiers, vol. 25, no. 2, pp. 723-742, 2023.

[9] A. Likas, N. Vlassis, and J. J. Verbeek, “The global k-means clustering algorithm”, Pattern recognition, vol. 36, no. 2, pp. 451-461, 2023.

[10] T. Mahmud, R,  Karim, R. Chakma, T. Chowdhury, M. S. Hossain, and K. Andersson, “A benchmark dataset for cricket sentiment analysis in bangla social media text”, Procedia Computer Science, vol. 238, pp. 377-384, 2024.

[11] M. K. Mali, R. R. Pawar, S. A. Shinde, S. D. Kale, S. V. Mulik A. A. Jagtap, and P. U. Rajput, “Automatic detection of cyberbullying behaviour on social media using Stacked Bi-Gru attention with BERT model”. Expert Systems with Applications, vol. 262, pp. 125641, 2025.

[12] M. Z. Naeem, F. Rustam, A. Mehmood, I. Ashraf, and G. S. Choi, “Classification of movie reviews using term frequency-inverse document frequency and optimized machine learning algorithms”, PeerJ Computer Science, vol. 8, e914, 2022.

[13] K. Nizam and N. Ahsan, “THE IMPACT OF E-DETERMINANTS ON CUSTOMER LOYALTY: A SOCIAL SUSTAINABILITY PARADIGM”, Journal of Research in Social Development and Sustainability, vol. 2, no. 2, pp. 87-115, 2023.

[14] D. Patel, S. Saxena, T. Verma, and P. G. Student, “Sentiment analysis using maximum entropy algorithm in big data”, International Journal of Innovative Research in Science, Engineering and Technology, vol. 5, no. 5, 2016.    

[15] K. Qian, A. Belyi, F. Wu, S. Khorshidi, A. Nikfarjam, R.  Khot, and Y. Li, “Open Domain Knowledge Extraction for Knowledge Graphs”, arXiv preprint arXiv:2312.09424, 2023.

[16] R. S. Rana, K. Jain, and M. Hema, “Information retrieval on disaster tweets using NLP”, In AIP Conference Proceedings, vol. 3075, no. 1, AIP Publishing, 2024.

[17] B. Rosenthal and Airoldi, “Consumer morality formation on social media platforms: the case of guns in Brazil”, Journal of micromarketing, vol. 44, no. 1, pp. 178-198, 2024.

[18] C. Song, S. Chen, X. Cai, and H. Chen, “Sentiment Analysis of Spanish Political Party Tweets Using Pre-Trained Language Models”, arXiv e-prints, arXiv-2411, 2024.

[19] S. Suryawanshi, B. R. Chakravarthi, P. Verma, M. Arcan, J. P. McCrae, and P. Buitelaar, “ A dataset for troll classification of TamilMemes”, In Proceedings of the WILDRE5–5th workshop on indian language data: resources and evaluation, pp. 7-13, 2020.

[20] W. Ullah, P. Oliveira-Silva, M. Nawaz, R. M.  Zulqarnain, I. Siddique, and M.  Sallah, “Identification of depressing tweets using natural language processing and machine learning: Application of grey relational grades”. Journal of Radiation Research and Applied Sciences, vol. 18, no. 1, 2025.

[21] S. Vyavahare, S. Teraiya, and S. Kumar, “FDM manufactured auxetic structures: an investigation of mechanical properties using machine learning techniques”. International Journal of Solids and Structures, vol. 265, pp. 112-126, 2023.

[22] L. Wang, “Support vector machines: theory and applications”, vol. 177, Springer Science & Business Media, 2005.