@inproceedings{f4e5a7f78290435ab892652e18c240f2,
title = "Classification of Stock Price Movement With Sentiment Analysis and Commodity Price: Case Study of Metals and Mining Sector",
abstract = "The unstable nature and complex behavior of the stock market make the prediction or forecasting process very difficult. The high level of debt and the declining price-earning ratio have bad implications for investment in metals and mining sector. This paper proposes a classification model for stock price movement based on financial news data, historical stock prices and commodity price data. We experiment with Support Vector Machine (SVM), Naive Bayes, and K-Nearest Neighbor (KNN) algorithm. The classifier then categorized the price into 'up', 'down', and 'constant'. The result shows that the best model is achieved by Naive Bayes Algorithm with an accuracy of 60% in three days period by combining copper price and sentiment analysis features.",
keywords = "data mining, mining industry, sentiment analysis, stock price prediction",
author = "Sinatrya, {Nadika Sigit} and Indra Budi and {Budi Santoso}, Aris",
note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 14th International Conference on Advanced Computer Science and Information Systems, ICACSIS 2022 ; Conference date: 01-10-2022 Through 03-10-2022",
year = "2022",
doi = "10.1109/ICACSIS56558.2022.9923452",
language = "English",
series = "Proceedings - ICACSIS 2022: 14th International Conference on Advanced Computer Science and Information Systems",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "59--64",
booktitle = "Proceedings - ICACSIS 2022",
address = "United States",
}