Bank Central Asia (BBCA) Stock Price Sentiment Analysis On Twitter Data Using Neural Convolutional Network (CNN) And Bidirectional Long Short-Term Memory (BI-LSTM)
DOI:
https://doi.org/10.30865/mib.v7i3.6120Keywords:
Convolutional Neural Network, Sentiment Analysis, Bidirectional Long Short-Term Memory, Twitter, StockAbstract
Stock investing has become popular among the public. Although this stock investment has significant risks, every year, investors keep increasing because the return from stocks is also quite promising. Social media also supports this stock investing, which can give information extensively and very quickly, so it can affect the stock price. The Efficient Market Hypothesis (EMH) theory defines that market information reflects stock prices. In this research, sentiment analysis uses a dataset crawled from Twitter to process the sentiment into helpful information. All the tweets related to stock prices are collected for sentiment analysis according to the appropriate sentiment type, whether it's a positive or negative sentiment. Many believe that sentiment influences stock price movements. This sentiment analysis process uses a hybrid method named Convolutional Neural Network (CNN) and Bidirectional Long Short-Term Memory (Bi-LSTM) with feature expansion Word2Vec. Afterwards, the hybrid method analysis will establish the final accuracy obtained. This research uses 27.930 data and shows the hybrid CNN Bi-LSTM method result is 95.74%.References
“PT Bursa Efek Indonesia.†https://www.idx.co.id/id/produk/saham (accessed Nov. 27, 2022).
A. Z. Malik, E. Utami, and S. Raharjo, “Analisis Sentiment Twitter Terhadap Capres Indonesia 2019 dengan Metode K-NN,†J. Inf. Politek. Indones. Surakarta, vol. 5, no. 2, pp. 1–7, 2019.
A. Kiky, S. Tinggi, and I. E. Wiyatamandala, “Kajian Empiris Teori Pasar Efisien (Efficient Market Hypothesis) Pada Bursa Efek Indonesia,†Maret, vol. 6, no. 2, pp. 138–156, 2018.
"Twitter Sentiment Analysis in Real-Time." https://monkeylearn.com/blog/sentiment-analysis-of-twitter/ (accessed Nov. 27, 2022).
“Analisis Sentimen (Sentiment Analysis) : Definisi, Tipe dan Cara Kerjanya.†https://lp2m.uma.ac.id/2022/02/21/analisis-sentimen-sentiment-analysis-definisi-tipe-dan-cara-kerjanya/ (accessed Nov. 27, 2022).
M. G. Pradana, A. C. Nurcahyo, P. H. Saputro, and K. Barat, “PERUSAHAAN STIM Shanti Bhuana Sentimen Negatif merupakan opini memiliki kesan kurang baik bagi pihak yang disebut , sebaliknya sentimen positif komplain adalah bentuk ekspresi formal tentang ketidaksukaan atau ketidakpuasan terhadap beberapa aspek yang di,†Ilmiah, J. Vol, Edutic, vol. 6, no. 2, 2020.
D. Yosmita Praptiwi, “Analisis Sentimen Online Review Pengguna E-Commerce Menggunakan Metode Support Vector Machine Dan Maximum Entropy,†2018.
“Kasus Pajak Bikin Saham Turun, Ini Respons Bos BCA - Saham Liputan6.com.†https://www.liputan6.com/saham/read/2040307/kasus-pajak-bikin-saham-turun-ini-respons-bos-bca (accessed Nov. 30, 2022).
C. Zhao, X. Huang, Y. Li, and M. Y. Iqbal, "A double-channel hybrid deep neural network based on CNN and BiLSTM for remaining useful life prediction," Sensors (Switzerland), vol. 20, no. 24, pp. 1–15, 2020, doi: 10.3390/s20247109.
“Cara Kerja Word2Vec. Apa itu Word2Vec? | by Afrizal Firdaus | Medium.†https://medium.com/@afrizalfir/mengenal-word2vec-af4758da6b5d (accessed Nov. 30, 2022).
"Advantages of word2vec | Python Natural Language Processing." https://subscription.packtpub.com/book/big-data-and-business-intelligence/9781787121423/6/ch06lvl1sec53/advantages-of-word2vec (accessed Nov. 30, 2022).
W. Yue and L. Li, "Sentiment Analysis using Word2vec-CNN-BiLSTM Classification," 2020 Seventh Int. Conf. Soc. Networks Anal. Manag. Secur., pp. 1–5, 2020, doi: 10.1109/SNAMS52053.2020.9336549.
K. Zhou and F. Long, "Sentiment analysis of text based on cnn and bi-directional LSTM model," ICAC 2018 - 2018 24th IEEE Int. Conf. Autom. Comput. Improv. Product. through Autom. Comput., no. September, pp. 1–5, 2018, doi: 10.23919/IConAC.2018.8749069.
S. Tam, R. Ben Said, and Ö. Tanriöver, "A ConvBiLSTM Deep Learning Model-Based Approach for Twitter Sentiment Classification," IEEE Access, vol. 9, pp. 41283–41293, 2021, doi: 10.1109/ACCESS.2021.3064830.
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., no. 0123456789, 2021, doi: 10.1007/s11277-021-08580-3.
D. T. Hermanto, A. Setyanto, and E. T. Luthfi, “Algoritma LSTM-CNN untuk Binary Klasifikasi dengan Word2vec pada Media Online,†Creat. Inf. Technol. J., vol. 8, no. 1, p. 64, 2021, doi: 10.24076/citec.2021v8i1.264.
I. S. Program, "Aspect-Based Sentiment Analysis on Twitter Using Long Short-Term Memory Method," vol. 7, pp. 1–10, 2023, doi: 10.30865/mib.v5i1.2293.
G. Kamil and E. B. Setiawan, "Aspect-Level Sentiment Analysis on Social Media Using Gated Recurrent Unit ( GRU )," vol. 99, no. 99, pp. 1–9, 2023, doi: 10.47065/bits.v9i9.999.
Z. Wang and Z. Qu, "Research on Web text classification algorithm based on improved CNN and SVM," Int. Conf. Commun. Technol. Proceedings, ICCT, vol. 2017-Octob, pp. 1958–1961, 2018, doi: 10.1109/ICCT.2017.8359971.
O. Setyawan and H. F. Pardede, "Sentiment analysis for event-based stock price predictions using bidirectional long short term memory," vol. 6, no. 1, pp. 50–58, 2022, doi: 10.52362/jisicom.v6i1.772.
M. A. Cifci, S. Hussain, and P. J. Canatalay, "Hybrid Deep Learning Approach for Accurate Tumor Detection in Medical Imaging Data," Diagnostics, vol. 13, no. 6, p. 1025, 2023, doi: 10.3390/diagnostics13061025.
A. K. J and S. Abirami, "Aspect-based opinion ranking framework for product reviews using a Spearman's rank correlation coefficient method," Inf. Sci. (Ny)., vol. 460–461, pp. 23–41, 2018, doi: 10.1016/j.ins.2018.05.003.
P. Schober and L. A. Schwarte, "Correlation coefficients: Appropriate use and interpretation," Anesth. Analg., vol. 126, no. 5, pp. 1763–1768, 2018, doi: 10.1213/ANE.0000000000002864.
Downloads
Published
Issue
Section
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:
- 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.
- 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.
- 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).
