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  2. Vol 4, No 2 (2023)
  3. Hariguna

Exploring the Flexibility and Accuracy of Sentiment Scoring Models through a Hybrid KNN-RNN-CNN Algorithm and ChatGPT

Taqwa Hariguna, Athapol Ruangkanjanases

Abstract


This study aimed to address the limitations of sentiment analysis by developing a more accurate and flexible sentiment scoring model using ChatGPT in combination with KNN, RNN, and CNN algorithms. To achieve this, primary data from ChatGPT and secondary data from Kaggle were utilized for training. The model's performance was evaluated, yielding an impressive accuracy rate of 88.17%. This research underscores ChatGPT's pivotal role in offering theoretical insights and precise data for diverse applications. The novelty of this study lies in its innovative approach of combining KNN, RNN, and CNN algorithms to create a more adaptable and accurate sentiment scoring model. Additionally, the primary data from ChatGPT greatly enhances the creation of precise and relevant training data across various topics and languages. Despite these achievements, there remains a need for further exploration of testing methods to mitigate the impact of data limitations on result generalizability. Moreover, it is acknowledged that the model's effectiveness may be diminished when applied to languages other than English. Nevertheless, this research provides a promising avenue for users seeking enhanced and precise sentiment analysis by integrating KNN, RNN, and CNN algorithms with ChatGPT. The findings of this study can serve as a solid foundation for future research endeavors in the advancement of sophisticated and effective sentiment analysis technologies.

 

Doi: 10.28991/HIJ-2023-04-02-06

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Keywords


Text Scoring; KNN; RNN; CNN; Sentiment Analysis; ChatGPT.

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.


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DOI: 10.28991/HIJ-2023-04-02-06

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