Exploring the Flexibility and Accuracy of Sentiment Scoring Models through a Hybrid KNN-RNN-CNN Algorithm and ChatGPT
Abstract
Doi: 10.28991/HIJ-2023-04-02-06
Full Text: PDF
Keywords
References
Shi, X., Wang, T., Wang, L., Liu, H., & Yan, N. (2019). Hybrid convolutional recurrent neural networks outperform CNN and RNN in Task-state EEG detection for Parkinson’s disease. 2019 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC). doi:10.1109/APSIPAASC47483.2019.9023190.
Kumar, A., & Garg, G. (2019). Sentiment analysis of multimodal twitter data. Multimedia Tools and Applications, 78(17), 24103–24119. doi:10.1007/s11042-019-7390-1.
Chakraborty, I., Kim, M., & Sudhir, K. (2022). Attribute Sentiment Scoring with Online Text Reviews: Accounting for Language Structure and Missing Attributes. Journal of Marketing Research, 59(3), 600–622. doi:10.1177/00222437211052500.
Li, J., Li, X., & He, D. (2019). A directed acyclic graph network combined with CNN and LSTM for remaining useful life prediction. IEEE Access, 7, 75464-75475. doi:10.1109/ACCESS.2019.2919566.
Praveen, S. V., & Vajrobol, V. (2023). Understanding the Perceptions of Healthcare Researchers Regarding ChatGPT: A Study Based on Bidirectional Encoder Representation from Transformers (BERT) Sentiment Analysis and Topic Modeling. Annals of Biomedical Engineering, 51(8), 1654–1656. doi:10.1007/s10439-023-03222-0.
Sudirjo, F., Diantoro, K., Al-Gasawneh, J. A., Khootimah Azzaakiyyah, H., & Almaududi Ausat, A. M. (2023). Application of ChatGPT in Improving Customer Sentiment Analysis for Businesses. Jurnal Teknologi Dan Sistem Informasi Bisnis, 5(3), 283–288. doi:10.47233/jteksis.v5i3.871.
Susnjak, T. (2023). Applying Bert and ChatGPT for sentiment analysis of Lyme disease in scientific literature. arXiv preprint arXiv:2302.06474. doi:10.48550/arXiv.2302.064474.
Sharma, S., Aggarwal, R., & Kumar, M. (2023). Mining Twitter for Insights into ChatGPT Sentiment: A Machine Learning Approach. 2023 International Conference on Distributed Computing and Electrical Circuits and Electronics (ICDCECE). doi:10.1109/icdcece57866.2023.10150620.
Wang, Z., Xie, Q., Ding, Z., Feng, Y., & Xia, R. (2023). Is ChatGPT a good sentiment analyzer? A preliminary study. arXiv preprint arXiv:2304.04339. doi:10.18550/arXiv.2304.04339.
Yang, K., Ji, S., Zhang, T., Xie, Q., & Ananiadou, S. (2023). On the evaluations of ChatGPT and emotion-enhanced prompting for mental health analysis. arXiv preprint arXiv:2304.03347. doi:10.48550/arXiv.2304.03347.
Haque, M. U., Dharmadasa, I., Sworna, Z. T., Rajapakse, R. N., & Ahmad, H. (2022). " I think this is the most disruptive technology": Exploring Sentiments of ChatGPT Early Adopters using Twitter Data. arXiv preprint arXiv:2212.05856. doi:10.48550/arXiv.2212.05856.
Fatouros, G., Soldatos, J., Kouroumali, K., Makridis, G., & Kyriazis, D. (2023). Transforming Sentiment Analysis in the Financial Domain with ChatGPT. arXiv preprint arXiv:2308.07935. doi:10.48550/arXiv.2308.07935.
Karanouh, M. (2023). Mapping ChatGPT in Mainstream Media: Early Quantitative Insights through Sentiment Analysis and Word Frequency Analysis. arXiv preprint arXiv:2305.18340. doi:10.48550/arXiv.2305.18340.
Saengrith, W., Viriyavejakul, C., & Pimdee, P. (2022). Problem-Based Blended Training via Chatbot to Enhance the Problem-Solving Skill in the Workplace. Emerging Science Journal, 6, 1-12. doi:10.28991/ESJ-2022-SIED-01.
Zhou, J., Meng, M., Gao, Y., Ma, Y., & Zhang, Q. (2018). Classification of motor imagery EEG using wavelet envelope analysis and LSTM networks. Chinese Control and Decision Conference (CCDC), IEEE, 5600-5605. doi:10.1109/CCDC.2018.8408108.
Bahmei, B., Birmingham, E., & Arzanpour, S. (2022). CNN-RNN and Data Augmentation Using Deep Convolutional Generative Adversarial Network for Environmental Sound Classification. IEEE Signal Processing Letters, 29, 682–686. doi:10.1109/LSP.2022.3150258.
Stephen, J. J., & Prabu, P. (2019). Detecting the magnitude of depression in Twitter users using sentiment analysis. International Journal of Electrical and Computer Engineering, 9(4), 3247–3255. doi:10.11591/ijece.v9i4.pp3247-3255.
Huang, J., Lin, S., Wang, N., Dai, G., Xie, Y., & Zhou, J. (2020). TSE-CNN: A Two-Stage End-to-End CNN for Human Activity Recognition. IEEE Journal of Biomedical and Health Informatics, 24(1), 292–299. doi:10.1109/JBHI.2019.2909688.
Kim, J., & Lee, M. (2014). Robust Lane Detection Based on Convolutional Neural Network and Random Sample Consensus. Lecture Notes in Computer Science, 454–461, Springer, Cham, Switzerland. doi:10.1007/978-3-319-12637-1_57.
Verma, P., Khanday, A. M. U. D., Rabani, S. T., Mir, M. H., & Jamwal, S. (2019). Twitter sentiment analysis on Indian government project using R. International Journal of Recent Technology and Engineering, 8(3), 8338–8341. doi:10.35940/ijrte.C6612.098319.
Asghar, M. Z., Sattar, A., Khan, A., Ali, A., Masud Kundi, F., & Ahmad, S. (2019). Creating sentiment lexicon for sentiment analysis in Urdu: The case of a resource-poor language. Expert Systems, 36(3), 12397. doi:10.1111/exsy.12397.
Hung, S. L., Kao, C. Y., & Huang, J. W. (2022). Constrained K-means and genetic algorithm-based approaches for optimal placement of wireless structural health monitoring sensors. Civil Engineering Journal, 8(12), 2675-2692. doi:10.28991/CEJ-2022-08-12-01.
Elzayady, H., Badran, K. M., & Salama, G. I. (2020). Arabic Opinion Mining Using Combined CNN - LSTM Models. International Journal of Intelligent Systems and Applications, 12(4), 25–36. doi:10.5815/ijisa.2020.04.03.
Behera, B., & Kumaravelan, G. (2021). Text document classification using fuzzy rough set based on robust nearest neighbor (FRS-RNN). Soft Computing, 25(15), 9915–9923. doi:10.1007/s00500-020-05410-9.
Wani, U. P., Gatagat, Y., & Thalor, M. Handwritten Character Recognition Using CNN, KNN and SVM. International Journal of Technology Engineering Arts Mathematics Science, 1(2), 2583-1224.
Pano, T., & Kashef, R. (2020). A complete Vader-based sentiment analysis of hum (BTC) tweets during the ERA of COVID-19. Big Data and Cognitive Computing, 4(4), 1–17. doi:10.3390/bdcc4040033.
Thelen, G. (2021). Leadership in a Global World Management Training Requirement Using the Example of the Asian Studies Program at University of Applied Sciences (HTWG) Konstanz. International Journal for Applied Information Management, 1(3), 125–135. doi:10.47738/ijaim.v1i3.14.
Al-Jedibi, W. (2022). The Strategic Plan of the Information Technology Deanship - King Abdulaziz University- Saudi Arabia. International Journal for Applied Information Management, 2(4), 84–94. doi:10.47738/ijaim.v2i4.40.
Endsuy, R. (2021). Sentiment Analysis between VADER and EDA for the US Presidential Election 2020 on Twitter Datasets. Journal of Applied Data Sciences, 2(1). doi:10.47738/jads.v2i1.17.
Riyanto, & Azis, A. (2021). Application of the Vector Machine Support Method in Twitter Social Media Sentiment Analysis Regarding the Covid-19 Vaccine Issue in Indonesia. Journal of Applied Data Sciences, 2(3), 102–108. doi:10.47738/jads.v2i3.40.
Efendi, A., Purwana, D., & Buchdadi, A. D. (2021). Human Capital Management of Government Internal Supervisory at the Ministry of Defense of the Republic Indonesia. International Journal for Applied Information Management, 2(2), 81–89. doi:10.47738/ijaim.v2i2.30.
Ye, E. Z., Ye, E. H., Bouthillier, M., & Ye, R. Z. (2022). DeepImageTranslator V2: Analysis of Multimodal Medical Images using Semantic Segmentation Maps Generated through Deep Learning. HighTech and Innovation Journal, 3(3), 319-325. doi:10.28991/HIJ-2022-03-03-07.
Aini, Q., Hammad, J. A. H., Taher, T., & Ikhlayel, M. (2021). Classification of Tweets Causing Deadlocks in Jakarta Streets with the Help of Algorithm C4.5. Journal of Applied Data Sciences, 2(4), 143–156. doi:10.47738/jads.v2i4.43.
Hananto, A. R., Rahayu, S. A., & Hariguna, T. (2021). An Ensemble and Filtering-Based System for Predicting Educational Data Mining. Journal of Applied Data Sciences, 2(4), 157–173. doi:10.47738/jads.v2i4.44.
Hariguna, T., Sukmana, H. T., & Kim, J. Il. (2020). Survey Opinion using Sentiment Analysis. Journal of Applied Data Sciences, 1(1), 35–40. doi:10.47738/jads.v1i1.10.
Hori, M. (2021). Study of Career Education for Women: Development of Global Human Resources. International Journal for Applied Information Management, 1(2), 11–20. doi:10.47738/ijaim.v1i2.9.
Musa, S., Muhyiddin, Y., & Nurhayati, S. (2022). Agriculturally based Equivalent Education: Insights on Nonformal Education Human Resources and Program Quality. Journal of Human, Earth, and Future, 3(4), 441-451. doi:10.28991/HEF-2022-03-04-04.
Hariguna, T., & Rachmawati, V. (2019). Community Opinion Sentiment Analysis on Social Media Using Naive Bayes Algorithm Methods. IJIIS: International Journal of Informatics and Information Systems, 2(1), 33–38. doi:10.47738/ijiis.v2i1.11.
DOI: 10.28991/HIJ-2023-04-02-06
Refbacks
- There are currently no refbacks.
Copyright (c) 2023 Taqwa Hariguna, Athapol Ruangkanjanases