Sentiment Analysis Classification of ChatGPT on Twitter Big Data in Indonesia Using Fast R-CNN

Authors

  • Sio Jurnalis Pipin Univeritas Mikroskil, Medan
  • Frans Mikael Sinaga Univeritas Mikroskil, Medan
  • Sunaryo Winardi Univeritas Mikroskil, Medan
  • Muhammad Noor Hakim Univeritas Mikroskil, Medan

DOI:

https://doi.org/10.30865/mib.v7i4.6816

Keywords:

ChatGPT, Sentiment Analysis, Fast R-CNN, Deep Learning, Twitter Data Analysis

Abstract

The advent of OpenAI's ChatGPT, a large language model (LLM) proficient in various fields including artificial intelligence (AI) and natural language processing (NLP), has ignited a plethora of opinions and discussions, especially on social media platforms like Twitter in Indonesia. This research seeks to delve into the intricate dynamics of these discussions, aiming to map both the commendations and criticisms surrounding ChatGPT's technological advancements and potential negative impacts. Utilizing deep learning-based sentiment analysis techniques, the study employs Convolutional Neural Network (CNN) and Fast Region-based Convolutional Network (Fast R-CNN) to analyze a dataset consisting of 7,604 tweets categorized into "Positive", "Negative", and "Neutral" sentiments. The objective is to provide a comprehensive understanding of the societal perceptions towards this artificial intelligence technology in the Indonesian context. The methodology encompasses several stages including data collection from Twitter, data cleaning, and pre-processing, followed by the application of CNN and Fast R-CNN models for sentiment analysis. The findings indicate a superior performance of the Fast R-CNN model, achieving an accuracy rate of 94.5%, compared to the CNN model with an accuracy rate of 86%. In conclusion, the research highlights the effectiveness of integrating Fast R-CNN in sentiment analysis to extract deeper insights from Twitter data in Indonesia. This study not only contributes to the scientific literature in the fields of sentiment analysis and natural language processing but also aids in formulating informed strategies to navigate the challenges and opportunities presented by artificial intelligence technology in the Indonesian landscape. Future research avenues should focus on optimizing this sentiment analysis model and exploring other potential applications of this technology in the dynamically evolving digital landscape in Indonesia.

Author Biographies

Sio Jurnalis Pipin, Univeritas Mikroskil, Medan

Department of information technology

Frans Mikael Sinaga, Univeritas Mikroskil, Medan

Department of information engineering

Sunaryo Winardi, Univeritas Mikroskil, Medan

Department of information engineering

Muhammad Noor Hakim, Univeritas Mikroskil, Medan

Department of information engineering

References

B. D. Lund, T. Wang, N. R. Mannuru, B. Nie, S. Shimray, and Z. Wang, “ChatGPT and a new academic reality: Artificial Intelligence-written research papers and the ethics of the large language models in scholarly publishing,†J Assoc Inf Sci Technol, vol. 74, no. 5, pp. 570–581, 2023, doi: https://doi.org/10.1002/asi.24750.

J. Kleesiek, Y. Wu, G. Stiglic, J. Egger, and J. Bian, “An Opinion on ChatGPT in Health Care-Written by Humans Only,†Journal of nuclear medicine : official publication, Society of Nuclear Medicine, vol. 64, no. 5. NLM (Medline), pp. 701–703, May 01, 2023. doi: 10.2967/jnumed.123.265687.

C. K. Lo, “What Is the Impact of ChatGPT on Education? A Rapid Review of the Literature,†Educ Sci (Basel), vol. 13, no. 4, Apr. 2023, doi: 10.3390/educsci13040410.

J. Lievens, “ARTIFICIAL INTELLIGENCE (AI) IN HIGHER EDUCATION: TOOL OR TRICKERY?,†in Education and New Developments, Jun. 2023, pp. 645–647. doi: 10.36315/2023v2end141.

Y. Akbar and T. Sugiharto, “Analisis Sentimen Pengguna Twitter di Indonesia Terhadap ChatGPT Menggunakan Algoritma C4.5 dan Naïve Bayes,†Jurnal Sains dan Teknologi, vol. 5, no. 1, pp. 115–122, 2023, doi: 10.55338/saintek.v4i3.1368.

M. Farrokhnia, S. K. Banihashem, O. Noroozi, and A. Wals, “A SWOT analysis of ChatGPT: Implications for educational practice and research,†Innovations in Education and Teaching International, 2023, doi: 10.1080/14703297.2023.2195846.

V. Taecharungroj, “‘What Can ChatGPT Do?’ Analyzing Early Reactions to the Innovative AI Chatbot on Twitter,†Big Data and Cognitive Computing, vol. 7, no. 1, Mar. 2023, doi: 10.3390/bdcc7010035.

G. Alexandridis, I. Varlamis, K. Korovesis, G. Caridakis, and P. Tsantilas, “A survey on sentiment analysis and opinion mining in greek social media,†Information (Switzerland), vol. 12, no. 8, Aug. 2021, doi: 10.3390/info12080331.

M. Bordoloi and S. K. Biswas, “Sentiment analysis: A survey on design framework, applications and future scopes,†Artif Intell Rev, pp. 1–56, Mar. 2023, doi: 10.1007/s10462-023-10442-2.

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, Dec. 2021, doi: 10.1007/s10796-021-10135-7.

S. J. Pipin and H. Kurniawan, “Analisis Sentimen Kebijakan MBKM Berdasarkan Opini Masyarakat di Twitter Menggunakan LSTM,†Jurnal SIFO Mikroskil, vol. 23, no. 2, pp. 197–208, 2022, doi: https://doi.org/10.55601/jsm.v23i2.900.

L. Yang, Y. Li, J. Wang, and R. S. Sherratt, “Sentiment Analysis for E-Commerce Product Reviews in Chinese Based on Sentiment Lexicon and Deep Learning,†IEEE, vol. 8, pp. 23522–23530, 2020, doi: 10.1109/ACCESS.2020.2969854.

U. D. Gandhi, P. Malarvizhi Kumar, G. Chandra Babu, and G. Karthick, “Sentiment Analysis on Twitter Data by Using Convolutional Neural Network (CNN) and Long Short Term Memory (LSTM),†Wirel Pers Commun, May 2021, doi: 10.1007/s11277-021-08580-3.

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, p. 2694, Mar. 2022, doi: 10.3390/app12052694.

A. A. Nagra, K. Alissa, T. M. Ghazal, S. Kukunuru, M. M. Asif, and M. Fawad, “Deep Sentiments Analysis for Roman Urdu Dataset Using Faster Recurrent Convolutional Neural Network Model,†Applied Artificial Intelligence, vol. 36, no. 1, Dec. 2022, doi: 10.1080/08839514.2022.2123094.

S. J. Pipin, R. Purba, and H. Kurniawan, “Prediksi Saham Menggunakan Recurrent Neural Network (RNN-LSTM) dengan Optimasi Adaptive Moment Estimation,†Journal of Computer System and Informatics (JoSYC), vol. 4, no. 4, pp. 806–815, 2023, doi: 10.47065/josyc.v4i4.4014.

S. Khairunnisa, A. Adiwijaya, and S. Al Faraby, “Pengaruh Text Preprocessing terhadap Analisis Sentimen Komentar Masyarakat pada Media Sosial Twitter (Studi Kasus Pandemi COVID-19),†JURNAL MEDIA INFORMATIKA BUDIDARMA, vol. 5, no. 2, p. 406, Apr. 2021, doi: 10.30865/mib.v5i2.2835.

Z.-H. Zhou, “A brief introduction to weakly supervised learning,†Natl Sci Rev, vol. 5, no. 1, pp. 44–53, Jan. 2018, doi: 10.1093/nsr/nwx106.

K. Maharana, S. Mondal, and B. Nemade, “A review: Data pre-processing and data augmentation techniques,†Global Transitions Proceedings, vol. 3, no. 1, pp. 91–99, Jun. 2022, doi: 10.1016/j.gltp.2022.04.020.

M. A. Rosid, A. S. Fitrani, I. R. I. Astutik, N. I. Mulloh, and H. A. Gozali, “Improving Text Preprocessing For Student Complaint Document Classification Using Sastrawi,†IOP Conf Ser Mater Sci Eng, vol. 874, no. 1, p. 012017, Jun. 2020, doi: 10.1088/1757-899X/874/1/012017.

H. N. Irmanda and Ria Astriratma, “Klasifikasi Jenis Pantun Dengan Metode Support Vector Machines (SVM),†Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi), vol. 4, no. 5, pp. 915–922, Oct. 2020, doi: 10.29207/resti.v4i5.2313.

M. M. Agüero-Torales, M. J. Cobo, E. Herrera-Viedma, and A. G. López-Herrera, “A cloud-based tool for sentiment analysis in reviews about restaurants on TripAdvisor,†Procedia Comput Sci, vol. 162, pp. 392–399, 2019, doi: 10.1016/j.procs.2019.12.002.

A. M. Barik, R. Mahendra, and M. Adriani, “Normalization of Indonesian-English Code-Mixed Twitter Data,†in Proceedings of the 5th Workshop on Noisy User-generated Text (W-NUT 2019), Stroudsburg, PA, USA: Association for Computational Linguistics, 2019, pp. 417–424. doi: 10.18653/v1/D19-5554.

G. M. Demirci, S. R. Keskin, and G. Dogan, “Sentiment Analysis in Turkish with Deep Learning,†in 2019 IEEE International Conference on Big Data (Big Data), IEEE, Dec. 2019, pp. 2215–2221. doi: 10.1109/BigData47090.2019.9006066.

J. Zhou, L. Liu, W. Wei, and J. Fan, “Network Representation Learning: From Preprocessing, Feature Extraction to Node Embedding,†ACM Comput Surv, vol. 55, no. 2, pp. 1–35, Feb. 2023, doi: 10.1145/3491206.

A. Patil, “Word Significance Analysis in Documents for Information Retrieval by LSA and TF-IDF using Kubeflow,†2022, pp. 335–348. doi: 10.1007/978-981-16-2126-0_29.

G. Katz et al., “The Marabou Framework for Verification and Analysis of Deep Neural Networks,†2019, pp. 443–452. doi: 10.1007/978-3-030-25540-4_26.

V. Shatravin, D. Shashev, and S. Shidlovskiy, “Applying the Reconfigurable Computing Environment Concept to the Deep Neural Network Accelerators Development,†in 2021 International Conference on Information Technology (ICIT), IEEE, Jul. 2021, pp. 842–845. doi: 10.1109/ICIT52682.2021.9491771.

Z. Abbasiantaeb and S. Momtazi, “Textâ€based question answering from information retrieval and deep neural network perspectives: A survey,†WIREs Data Mining and Knowledge Discovery, vol. 11, no. 6, Nov. 2021, doi: 10.1002/widm.1412.

Z. Zhong, L. Sun, and Q. Huo, “An anchor-free region proposal network for Faster R-CNN-based text detection approaches,†International Journal on Document Analysis and Recognition (IJDAR), vol. 22, no. 3, pp. 315–327, Sep. 2019, doi: 10.1007/s10032-019-00335-y.

Y. Xiao, X. Wang, P. Zhang, F. Meng, and F. Shao, “Object Detection Based on Faster R-CNN Algorithm with Skip Pooling and Fusion of Contextual Information,†Sensors, vol. 20, no. 19, p. 5490, Sep. 2020, doi: 10.3390/s20195490.

G. B. Loganathan, T. H. Fatah, E. T. Yasin, and N. I. Hamadamen, “To Develop Multi-Object Detection and Recognition Using Improved GP-FRCNN Method,†in 2022 8th International Conference on Smart Structures and Systems (ICSSS), IEEE, Apr. 2022, pp. 1–7. doi: 10.1109/ICSSS54381.2022.9782296.

Downloads

Published

2023-10-31

Issue

Vol. 7 No. 4 (2023): Oktober 2023

Section

Articles

License

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License

Authors who publish with this journal agree to the following terms:

  1. Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under Creative Commons Attribution 4.0 International License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
  2. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
  3. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (Refer to The Effect of Open Access).