Sentiment Analysis of Indonesian Economics News Summary on The ICI using Long Short Term Memory
- Muhammad Raihan Muhith Telkom University, Bandung, Indonesia
-
Imelda Atastina
*
Telkom University, Bandung,
Indonesia
(*) Corresponding Author
Abstract
Many investors use news summary as a reference while deciding whether to purchase, sell, or hold shares as part of their investment activity. The information provided by the news, on the other hand, frequently fails to meet expectations. Therefore, this study aims to classify economic news summary using sentiment analysis method followed by analyzing the results to determine the correlation between Indonesian economic news summaries and Indonesia Composite Index (ICI) trends. The dataset for this study is a collection of news articles from the Kontan online news site. This study begins with the collection of a dataset that includes 1720 training data and 440 test data that have been automatically summarized using the cosine similarity approach and labeled (positive/negative) based on the ICI trend the next day after the data was crawled. We use Term Frequency – Inverse Document Frequency (TF-IDF) as the word weighting technique and Long Short Term Memory (LSTM) method as the classifier to build the model. From the model training process that has been carried out for eight trials by tuning hyperparameters, the training accuracy is 86% and the F1-Score is 89.3%. While the testing accuracy and F1-score are 68.1% and 75.8% consecutively
Downloads
References
G. N. Arviana, “Sebelum Investasi, Pahami Dulu Apa Itu IHSG dan Berbagai Fungsinya,” 8 March, 2021. https://glints.com/id/lowongan/apa-itu-ihsg/#.YsBDAWhBxPZ (accessed Jul. 02, 2022).
R. Budiman, “Strategi Membeli dan Menjual,” in Investing is Easy Teknik Analisa dan Strategi Investasi Saham untuk Pemula, Fathur, Ed. Jakarta: PT Elex Media Komputindo, 2017, p. 84.
W. Medhat, A. Hassan, and H. Korashy, “Sentiment analysis algorithms and applications: A survey,” Ain Shams Eng. J., vol. 5, no. 4, 2014, doi: 10.1016/j.asej.2014.04.011.
S. Mohan, S. Mullapudi, S. Sammeta, P. Vijayvergia, and D. C. Anastasiu, “Stock price prediction using news sentiment analysis,” 2019, doi: 10.1109/BigDataService.2019.00035.
K. Joshi, B. H. N, and J. Rao, “Stock Trend Prediction Using News Sentiment Analysis,” Int. J. Comput. Sci. Inf. Technol., vol. 8, no. 3, 2016, doi: 10.5121/ijcsit.2016.8306.
R. Gupta and M. Chen, “Sentiment Analysis for Stock Price Prediction,” 2020, doi: 10.1109/MIPR49039.2020.00051.
A. Goud and B. Garg, “Sentiment Analysis Using Long Short-Term Memory Model in Deep Learning,” 2021, doi: 10.1007/978-3-030-47560-4_2.
F. Miedema, “Sentiment analysis with long short-term memory networks,” Vrije Univ. Amsterdam, vol. 1, 2018.
D. Li and J. Qian, “Text sentiment analysis based on long short-term memory,” 2016, doi: 10.1109/CCI.2016.7778967.
R. Ferreira, R. D. Lins, F. Freitas, S. J. Simske, and M. Riss, “A new sentence similarity assessment measure based on a three-layer sentence representation,” 2014, doi: 10.1145/2644866.2644881.
N. Moratanch and S. Chitrakala, “A survey on extractive text summarization,” 2017, doi: 10.1109/ICCCSP.2017.7944061.
M. S. Pawar and S. Rathod, “Text summarization using cosine similarity and clustering approach,” EasyChair, 2020.
R. H. Singh, S. Maurya, T. Tripathi, T. Narula, and G. Srivastav, “Movie recommendation system using cosine similarity and KNN,” Int. J. Eng. Adv. Technol., vol. 9, no. 5, pp. 556–559, 2020.
S. Qaiser and R. Ali, “Text Mining: Use of TF-IDF to Examine the Relevance of Words to Documents,” Int. J. Comput. Appl., vol. 181, no. 1, 2018, doi: 10.5120/ijca2018917395.
M. R. Pratama, F. A. G. Soerawinata, R. R. Zhafari, and H. N. Irmanda, “Sentiment Analysis of Beauty Product E-Commerce Using Support Vector Machine Method,” J. RESTI (Rekayasa Sist. Dan Teknol. Informasi), vol. 6, no. 2, pp. 269–274, 2022.
Z. Zhao, W. Chen, X. Wu, P. C. Y. Chen, and J. Liu, “LSTM network: A deep learning approach for Short-term traffic forecast,” IET Intell. Transp. Syst., vol. 11, no. 2, 2017, doi: 10.1049/iet-its.2016.0208.
C. C. Aggarwal, Neural Networks and Deep Learning: A Textbook. 2018.
R. C. Staudemeyer and E. R. Morris, “Understanding LSTM - a tutorial into Long Short-Term Memory Recurrent Neural NetworksNo Title,” CoRR, vol. abs/1909.0, 2019, [Online]. Available: http://arxiv.org/abs/1909.09586.
T. B. Shahi, A. Shrestha, A. Neupane, and W. Guo, “Stock price forecasting with deep learning: A comparative study,” Mathematics, vol. 8, no. 9, 2020, doi: 10.3390/math8091441.
A. Nurkholis, D. Alita, and A. Munandar, “Comparison of Kernel Support Vector Machine Multi-Class in PPKM Sentiment Analysis on Twitter,” J. RESTI (Rekayasa Sist. Dan Teknol. Informasi), vol. 6, no. 2, pp. 227–233, 2022.
Bila bermanfaat silahkan share artikel ini
Berikan Komentar Anda terhadap artikel Sentiment Analysis of Indonesian Economics News Summary on The ICI using Long Short Term Memory
Pages: 171-180
Copyright (c) 2022 Muhammad Raihan Muhith, Imelda Atastina
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).
