[1] M. Ahmad, S. Aftab, I. Ali, and N. Hameed, “Hybrid tools and techniques for sentiment analysis: a review,” International Journal of Multidisciplinary Sciences and Engineering, vol. 8, no. 3, pp. 29–33 (2017).[2] K. Gandhe, A. S. Varde, and X. Du, “Sentiment analysis of Twitter data with hybrid learning for recommender applications,” in Proc. 9th IEEE Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON), New York, NY, USA, pp. 57–63 (Nov. 2018).[3] N. Zainuddin, A. Selamat, and R. Ibrahim, “Hybrid sentiment classification on Twitter aspect-based sentiment analysis,” Applied Intelligence, vol. 48, pp. 1218–1232 (2018).[4] S. J. Putra, I. Khalil, M. N. Gunawan, R. I. Amin, and T. Sutabri, “A hybrid model for social media sentiment analysis for Indonesian text,” in Proc. 20th Int. Conf. Information Integration and Web-Based Applications & Services, Yogyakarta, Indonesia, pp. 297–301 (Nov. 2018).[5] O. Appel, F. Chiclana, J. Carter, and H. Fujita, “Successes and challenges in developing a hybrid approach to sentiment analysis,” Applied Intelligence, vol. 48, pp. 1176–1188 (2018).[6] T. Handhika, A. Fahrurozi, I. Sari, D. P. Lestari, and R. I. M. Zen, “Hybrid method for sentiment analysis using homogeneous ensemble classifier,” in Proc. 2nd Int. Conf. Computer and Informatics Engineering (IC2IE), Bandung, Indonesia, pp. 232–236 (Sep. 2019).[7] H. K. Menaria, P. Nagar, and M. Patel, “Tweet sentiment classification by semantic and frequency base features using the hybrid classifier,” in Proc. 1st Int. Conf. Sustainable Technologies for Computational Intelligence (ICTSCI), Singapore: Springer, pp. 107–123 (Nov. 2019).[8] M. A. Hassonah, R. Al-Sayyed, A. Rodan, A. Z. Ala’M, I. Aljarah, and H. Faris, “An efficient hybrid filter and evolutionary wrapper approach for sentiment analysis of various topics on Twitter,” Knowledge-Based Systems, vol. 192, 105353 (2020).[9] A. H. Sweidan, N. El-Bendary, and H. Al-Feel, “Sentence-level aspect-based sentiment analysis for classifying adverse drug reactions (ADRs) using hybrid ontology-XLNet transfer learning,” IEEE Access, vol. 9, pp. 90828–90846 (2021).[10] S. Park, L. M. Bier, and H. W. Park, “The effects of infotainment on public reaction to North Korea using hybrid text mining: Content analysis, machine learning-based sentiment analysis, and co-word analysis,” Profesional de la Información, vol. 30, no. 3 (2021).[11] S. Soubraylu and R. Rajalakshmi, “Hybrid convolutional bidirectional recurrent neural network-based sentiment analysis on movie reviews,” Computational Intelligence, vol. 37, no. 2, pp. 735–757 (2021).[12] T. T. Mengistie and D. Kumar, “Deep learning-based sentiment analysis on COVID-19 public reviews,” in Proc. Int. Conf. Artificial Intelligence in Information and Communication (ICAIIC), Jeju Island, Korea (South), pp. 444–449 (Apr. 2021).[13] H. Naz, S. Ahuja, D. Kumar, and Rishu, “DT-FNN-based effective hybrid classification scheme for Twitter sentiment analysis,” Multimedia Tools and Applications, vol. 80, pp. 11443–11458 (2021).[14] R. Singh, G. Joshi, and P. Kothari, “A comparative analysis with the hybrid algorithm approach for sentimental analysis through machine learning,” in Proc. Int. Conf. Data Science, Machine Learning and Artificial Intelligence, pp. 290–295 (Aug. 2021).[15] H. Ezaldeen, R. Misra, S. K. Bisoy, R. Alatrash, and R. Priyadarshini, “A hybrid E-learning recommendation integrating adaptive profiling and sentiment analysis,” Journal of Web Semantics, vol. 72, 100700 (2022).[16] A. Alasmari, A. Alhothali, and A. Allinjawi, “Hybrid machine learning approach for Arabic medical web page credibility assessment,” Health Informatics Journal, vol. 28, no. 1, 14604582211070998 (2022).[17] M. M. Kabir, Z. A. Othman, M. R. Yaakub, and S. Tiun, “Hybrid Syntax Dependency with Lexicon and Logistic Regression for Aspect-based Sentiment Analysis,” Int. J. Advanced Computer Science and Applications, vol. 14, no. 10 (2023).[18] M. Kuang, R. Safa, S. A. Edalatpanah, and R. S. Keyser, “A hybrid deep learning approach for sentiment analysis in product reviews,” Facta Universitatis, Series: Mechanical Engineering, vol. 21, no. 3, pp. 479–500 (2023).[19] R. Venkatesan and A. Sabari, “Deepsentimodels: A novel hybrid deep learning model for an effective analysis of ensembled sentiments in e-commerce and s-commerce platforms,” Cybernetics and Systems, vol. 54, no. 4, pp. 526–549 (2023).[20] M. R. R. Rana, S. U. Rehman, A. Nawaz, T. Ali, A. Imran, A. Alzahrani, and A. Almuhaimeed, “Aspect-Based Sentiment Analysis for Social Multimedia: A Hybrid Computational Framework,” Computer Systems Science & Engineering, vol. 46, no. 2, pp. 2415–2428 (2023).[21] M. Sharaf, E. E. D. Hemdan, A. El-Sayed, and N. A. El-Bahnasawy, “An efficient hybrid stock trend prediction system during the COVID-19 pandemic based on stacked-LSTM and news sentiment analysis,” Multimedia Tools and Applications, vol. 82, no. 16, pp. 23945–23977 (2023).[22] T. Hussain, L. Yu, M. Asim, A. Ahmed, and M. A. Wani, “Enhancing E-Learning Adaptability with Automated Learning Style Identification and Sentiment Analysis: A Hybrid Deep Learning Approach for Smart Education,” Information, vol. 15, no. 5, p. 277 (2024).[23] D. Nurmalasari, D. H. Qudsi, N. Chairani, and H. R. Yuliantoro, “Discovering User Sentiment Patterns in Libraries with a Hybrid Machine Learning and Lexicon-Based Approach,” Jurnal Sisfokom (Sistem Informasi dan Komputer), vol. 13, no. 3, pp. 311–317 (2024).[24] P. Devika and A. Milton, “Book recommendation using sentiment analysis and ensembling hybrid deep learning models,” Knowledge and Information Systems, pp. 1–38 (2024).[25] R. Vatambeti, S. V. Mantena, K. V. D. Kiran, M. Manohar, and C. Manjunath, “Twitter sentiment analysis on online food services based on elephant herd optimization with hybrid deep learning technique,” Cluster Computing, vol. 27, no. 1, pp. 655–671 (2024).[26] F. Es-sabery, I. Es-sabery, J. Qadir, B. Sainz-de-Abajo, and B. Garcia-Zapirain, “A hybrid Hadoop-based sentiment analysis classifier for tweets associated with COVID-19 utilizing two machine learning algorithms: CNN, and fuzzy C4.5,” Journal of Big Data, vol. 11, no. 1, p. 176 (2024).[27] N. L. Devi, B. Anilkumar, A. M. Sowjanya, and S. Kotagiri, “An innovative word-embedded and optimization-based hybrid artificial intelligence approach for aspect-based sentiment analysis of app and cellphone reviews,” Multimedia Tools and Applications, pp. 1–34 (2024).[28] V. Jain, G. Chaudhary, N. Luthra, A. Rao, and S. Walia, “Dynamic handwritten signature and machine learning based identity verification for keyless cryptocurrency transactions,” Journal of Discrete Mathematical Sciences and Cryptography, vol. 22, no. 2, pp. 191–202 (Mar. 2019).[29] V. Jain, P. S. Lamba, B. Singh, N. Namboothiri, and S. Dhall, “Facial expression recognition using feature level fusion,” Journal of Discrete Mathematical Sciences and Cryptography, vol. 22, no. 2, pp. 337–350 (Mar. 2019).[30] V. Jain, D. Saini, and A. Ahluwalia, “Real-time autonomous trading system,” Journal of Statistics and Management Systems, vol. 22, no. 2, pp. 403–413 (Mar. 2019).[31] A. Jain and V. Jain, “Sentiment classification of Twitter data belonging to renewable energy using machine learning,” Journal of Information and Optimization Sciences, vol. 40, no. 2, pp. 521–533 (Mar. 2019).