References
- V. Mahalakshmi, P. Shenbagavalli, S. Raguvaran, V. Rajakumareswaran and E. Sivaraman, “Twitter sentiment analysis using conditional generative adversarial network.” International Journal of Cognitive Computing in Engineering, vol. 5, pp. 161–169, 2024.
- A. Alsayat, “Improving sentiment analysis for social media applications using an ensemble deep learning language model.” Arabian Journal for Science and Engineering, vol. 47, no. 2, pp. 2499–2511, 2022.
- S.T. Kokab, S. Asghar and S. Naz, “Transformer-based deep learning models for the sentiment analysis of social media data.” Array, vol. 14, pp. 100157, 2022.
- L. Khan, A. Amjad, K.M. Afaq and H.T. Chang, “Deep sentiment analysis using CNN-LSTM architecture of English and Roman Urdu text shared in social media.” Applied Sciences, vol. 12, no. 5, pp. 2694, 2022.
- R. Obiedat, R. Qaddoura, A.Z. Ala'M, L. Al-Qaisi, O. Harfoushi, M.A. Alrefai and H. Faris, “Sentiment analysis of customers' reviews using a hybrid evolutionary SVM-based approach in an imbalanced data distribution.” IEEE Access, vol. 10, pp. 22260–22273, 2022.
- N. Aslam, F. Rustam, E. Lee, P.B. Washington and Ashraf I, “Sentiment analysis and emotion detection on cryptocurrency related tweets using ensemble LSTM-GRU model.” Ieee Access, vol. 10, pp. 39313–39324, 2022.
- M. Loukili, F. Messaoudi and M. El Ghazi, “Sentiment Analysis of Product Reviews for E-Commerce Recommendation based on Machine Learning.” International Journal of Advances in Soft Computing & Its Applications, vol. 15, no. 1.
- C. Ahmed, A. ElKorany and E. ElSayed, “Prediction of customer's perception in social networks by integrating sentiment analysis and machine learning.” Journal of Intelligent Information Systems, vol. 60, no. 3, pp. 829–851, 2023.
- G. Revathy, S.A. Alghamdi, S.M. Alahmari, S.R. Yonbawi, A. Kumar and M.A. Haq, “Sentiment analysis using machine learning: Progress in the machine intelligence for data science.” Sustainable Energy Technologies and Assessments, vol. 53, pp. 102557, 2022.
- W. Aljedaani, F. Rustam, M.W. Mkaouer, A. Ghallab, V. Rupapara, P.B. Washington, E. Lee and I. Ashraf, “Sentiment analysis on Twitter data integrating TextBlob and deep learning models: The case of US airline industry.” Knowledge-Based Systems, vol. 255, pp. 109780, 2022.
- N. Chintalapudi, G. Battineni and F. Amenta, “Sentimental analysis of COVID-19 tweets using deep learning models.” Infectious disease reports, vol. 13, no. 2, pp. 329–339, 2021.
- C.S.R. Priya and P. Deepalakshmi, “Sentiment analysis from unstructured hotel reviews data in social network using deep learning techniques.” International Journal of Information Technology, vol. 15, no. 7, pp. 3563–3574, 2023.
- H. Kaur, S.U. Ahsaan, B. Alankar and V. Chang, “A proposed sentiment analysis deep learning algorithm for analyzing COVID-19 tweets.” Information Systems Frontiers, vol. 23, no. 6, pp. 1417–1429, 2021.
- U. Sehar, S. Kanwal, K. Dashtipur, U. Mir, U. Abbasi and F. Khan, “Urdu sentiment analysis via multimodal data mining based on deep learning algorithms.” IEEE Access, vol. 9, pp. 153072–153082, 2021.
- A. Baqach and A. Battou, “CLAS: A new deep learning approach for sentiment analysis from Twitter data.” Multimedia Tools and Applications, vol. 82, no. 30, pp. 47457–47475, 2023.
- H.T. Halawani, A.M. Mashraqi, S.K. Badr and S. Alkhalaf, “Automated sentiment analysis in social media using Harris Hawks optimisation and deep learning techniques.” Alexandria Engineering Journal, vol. 80, pp. 433–443, 2023.
- S. Aslan, S. Kızıloluk and E. Sert, “TSA-CNN-AOA: Twitter sentiment analysis using CNN optimized via arithmetic optimization algorithm.” Neural Computing and Applications, vol. 35, no. 14, pp. 10311–10328, 2023.
- N. Parveen, P. Chakrabarti, B.T. Hung and A. Shaik, “Twitter sentiment analysis using hybrid gated attention recurrent network.” Journal of Big Data, vol. 10, no. 1, pp. 50, 2023.
- K. P. Vidyashree and A.B. Rajendra, “An improvised sentiment analysis model on twitter data using stochastic gradient descent (SGD) optimization algorithm in stochastic gate neural network (SGNN).” SN computer science, vol. 4, no. 2, pp. 190, 2023.
- Serpil Aslan, A deep learning-based sentiment analysis approach (MF-CNN-BILSTM) and topic modeling of tweets related to the Ukraine–Russia conflict, Applied Soft Computing, Vol. 143, 2023, 110404, ISSN 1568-4946,
https://doi.org/10.1016/j.asoc.2023.110404 . - Goismi Mohamed, Hamou Reda Mohamed, Tomouh Adil, Maaskri Moustafa Enhancing Twitter Sentiment Classification with a Hybrid Bio-Inspired Feature Selection Approach” Vol. 10 No. 53s (2025) e-ISSN:2468-4376, JISEM.
- S. Hundekari, S.A.V. Nair, P. Shanthi, C.M. Rao, M. Rafeeq, T.B. Sivakumar, “Turbocharged AI: Harnessing Federated Learning and Model Parallelism for Efficient Deep Learning on Distributed System,” Communications on Applied Nonlinear Analysis, vol. 31, 2024.
- M. D. Rafeeq, V.H. Shastri, A. Ba, V. Shanmugasundaram, P.R. Kumar, D. Jyothsna, “An Intelligent Road Traffic Data Management for Upgrading Real Time Performance,” AIP Conference Proceedings, vol. 2426, 2022.