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Original Article
Social Media Sentiment Analyzer
Amtullah Mohammed1
Dr. Khaja Mahabubullah2
1 Student, MCA Deccan College of Engineering and Technology, Hyderabad, Telangana, India. 2 Professor & HOD, MCA Deccan College of Engineering and Technology, Hyderabad, Telangana, India.
Published Online: July-August 2025
Pages: 48-52
Cite this article
↗ https://www.doi.org/10.59256/ijire.20250604008
References
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2. A. Go, R. Bhayani, and L. Huang, “Twitter Sentiment Classification using Distant Supervision,” Stanford University, CS224N Project Report,2009.
3. T. Mikolov, K. Chen, G. Corrado, and J. Dean, “Efficient Estimation of Word Representations in Vector Space,” arXiv preprint, arXiv: 1301.3781,2013.
4. J. Pennington, R. Socher, and C. D. Manning, “GloVe: Global Vectors for Word Representation,” in Proc. EMNLP, pp. 1532–1543, 2014.
5. F. Pedregosa et al., “Scikit-learn: Machine Learning in Python,” Journal of Machine Learning Research, vol. 12, pp. 2825–2830, 2011.
6. J. Howard and S. Gugger, “Fastai: A Layered API for Deep Learning,” Information, vol. 11, no. 2, p. 108, 2020. doi: 10.3390/info11020108.
7. T. Chen and C. Guestrin, “XGBoost: A Scalable Tree Boosting System,” in Proc. 22nd ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining, pp. 785–794, 2016.
8. W. McKinney, “Data Structures for Statistical Computing in Python,” in Proc. of the 9th Python in Science Conference, pp. 51–56, 2010.
9. Streamlit Team, “Streamlit Documentation,” [Online]. Available: https://docs.streamlit.io/
10. scikit-learn developers, “scikit-learn: Machine Learning in Python,” [Online]. Available: https://scikit-learn.org/
11. S. Bird, E. Klein, and E. Loper, Natural Language Processing with Python, O’Reilly Media, 2009.
12. S. Ruder, “An Overview of Multi-Task Learning in Deep Neural Networks,” arXiv preprint, arXiv: 1706.05098, 2017.
13. OpenAI, “OpenAI API,” [Online]. Available:
14. D. Jurafsky and J. H. Martin, Speech and Language Processing, 3rd ed., Pearson, 2023.
15. H. Zhang, “The Optimality of Naive Bayes,” AA, vol. 1, no. 2, pp. 1–6, 2004.
2. A. Go, R. Bhayani, and L. Huang, “Twitter Sentiment Classification using Distant Supervision,” Stanford University, CS224N Project Report,2009.
3. T. Mikolov, K. Chen, G. Corrado, and J. Dean, “Efficient Estimation of Word Representations in Vector Space,” arXiv preprint, arXiv: 1301.3781,2013.
4. J. Pennington, R. Socher, and C. D. Manning, “GloVe: Global Vectors for Word Representation,” in Proc. EMNLP, pp. 1532–1543, 2014.
5. F. Pedregosa et al., “Scikit-learn: Machine Learning in Python,” Journal of Machine Learning Research, vol. 12, pp. 2825–2830, 2011.
6. J. Howard and S. Gugger, “Fastai: A Layered API for Deep Learning,” Information, vol. 11, no. 2, p. 108, 2020. doi: 10.3390/info11020108.
7. T. Chen and C. Guestrin, “XGBoost: A Scalable Tree Boosting System,” in Proc. 22nd ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining, pp. 785–794, 2016.
8. W. McKinney, “Data Structures for Statistical Computing in Python,” in Proc. of the 9th Python in Science Conference, pp. 51–56, 2010.
9. Streamlit Team, “Streamlit Documentation,” [Online]. Available: https://docs.streamlit.io/
10. scikit-learn developers, “scikit-learn: Machine Learning in Python,” [Online]. Available: https://scikit-learn.org/
11. S. Bird, E. Klein, and E. Loper, Natural Language Processing with Python, O’Reilly Media, 2009.
12. S. Ruder, “An Overview of Multi-Task Learning in Deep Neural Networks,” arXiv preprint, arXiv: 1706.05098, 2017.
13. OpenAI, “OpenAI API,” [Online]. Available:
14. D. Jurafsky and J. H. Martin, Speech and Language Processing, 3rd ed., Pearson, 2023.
15. H. Zhang, “The Optimality of Naive Bayes,” AA, vol. 1, no. 2, pp. 1–6, 2004.
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