AI-Powered Sentiment Analysis: Understanding Consumer Opinions in Social Media
Abstract
Sentiment analysis has become a key tool for understanding consumer opinions in social media. This paper explores AI-powered sentiment analysis using supervised and unsupervised machine learning techniques, including LSTM, CNN, and transformer models. We evaluate their performance on social media datasets and propose improvements for real-time sentiment tracking.
References
Baars, H., & Kemper, H.-G. (2008). Management support with structured and unstructured data—An integrated business intelligence framework. Information Systems Management, 25(2), 132–148. https://doi.org/10.1080/10580530801941058
Bengio, Y., Courville, A., & Vincent, P. (2013). Representation learning: A review and new perspectives. IEEE Transactions on Pattern Analysis and Machine Intelligence, 35(8), 1798–1828. https://doi.org/10.1109/TPAMI.2013.50
Nadella, G. S., Satish, S., Meduri, K., & Meduri, S. S. (2023). A Systematic Literature Review of Advancements, Challenges and Future Directions of AI And ML in Healthcare. International Journal of Machine Learning for Sustainable Development, 5(3), 115-130.
Brown, T. B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., & Amodei, D. (2020). Language models are few-shot learners. Advances in Neural Information Processing Systems, 33, 1877–1901. https://doi.org/10.48550/arXiv.2005.14165
Chen, T., & Guestrin, C. (2016). XGBoost: A scalable tree boosting system. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 785–794). https://doi.org/10.1145/2939672.2939785
Maturi, M. H., Satish, S., Gonaygunta, H., & Meduri, K. (2022). The Intersection of Artificial Intelligence and Neuroscience: Unlocking the Mysteries of the Brain. International Journal of Creative Research In Computer Technology and Design, 4(4), 1-21.
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). ImageNet classification with deep convolutional neural networks. In Advances in Neural Information Processing Systems (Vol. 25, pp. 1097–1105). https://doi.org/10.1145/3065386
LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436–444. https://doi.org/10.1038/nature14539
Li, H., & Liu, X. (2018). Federated learning: Challenges, methods, and future directions. IEEE Communications Magazine, 56(12), 34–40. https://doi.org/10.1109/MCOM.2018.1800258
Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.-Y., & Berg, A. C. (2016). SSD: Single shot multibox detector. In Proceedings of the European Conference on Computer Vision (pp. 21–37). https://doi.org/10.1007/978-3-319-46448-0_2
Radford, A., Narasimhan, K., Salimans, T., & Sutskever, I. (2018). Improving language understanding by generative pre-training. OpenAI Research Paper. https://doi.org/10.48550/arXiv.1801.06146
Ruder, S. (2019). Transfer learning—Machine learning’s next frontier. Machine Learning Research (Vol. 1). https://doi.org/10.48550/arXiv.1810.04805
Maturi, M. H., Gonaygunta, H., Nadella, G. S., & Meduri, K. (2023). Fault Diagnosis and Prognosis using IoT in Industry 5.0. International Numeric Journal of Machine Learning and Robots, 7(7), 1-21.
Silver, D., Huang, A., Maddison, C. J., Guez, A., Sifre, L., van den Driessche, G., & Hassabis, D. (2016). Mastering the game of Go with deep neural networks and tree search. Nature, 529(7587), 484–489. https://doi.org/10.1038/nature16961
Simonyan, K., & Zisserman, A. (2015). Very deep convolutional networks for large-scale image recognition. International Conference on Learning Representations. https://doi.org/10.48550/arXiv.1409.1556
