A Predictive Analytic on Data Online Digital News using Systematic Literature Review

Razief Perucha Fauzie Afidh, Zainal A. Hasibuan

Research output: Contribution to journalConference articlepeer-review

Abstract

This study intents to provide an overview of the use of online digital news as a text dataset for future data analysis. Systematic literature review used as the method for collecting and analyze the information from previous study that used online digital news as a dataset. The result showed that the used of online digital news as a dataset can be implemented for classification and clustering. Furthermore, online digital news dataset is used to predict stock price and product price movement, to predict the approval rate for election process, to analyze the diseases epidemiology, to detect event, classification of fakes news, popularity of news in social media and other NLP tasks. By comparing online digital news dataset versus social media dataset, it can be used to detect fake news, news popularity prediction, stock price prediction, topic detection, sentiment analysis, event detection and prediction, spam detection, trending topic prediction and other task. Online digital news as a text dataset has a powerful performance to be implemented in the various field such as economics, political, health, language and so forth.

Original languageEnglish
Article number012094
JournalIOP Conference Series: Materials Science and Engineering
Volume879
Issue number1
DOIs
Publication statusPublished - 5 Aug 2020
Event3rd International Conference on Informatics, Engineering, Science, and Technology, INCITEST 2020 - Bandung, Virtual, Indonesia
Duration: 11 Jun 2020 → …

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