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News based Stock Market Sentiment Lexicon Acquisition Using Word2Vec  

Kim, Daye (연세대학교 정보대학원)
Lee, Youngin (연세대학교 정보대학원)
Publication Information
The Journal of Bigdata / v.3, no.1, 2018 , pp. 13-20 More about this Journal
Abstract
Stock market prediction has been long dream for researchers as well as the public. Forecasting ever-changing stock market, though, proved a Herculean task. This study proposes a novel stock market sentiment lexicon acquisition system that can predict the growth (or decline) of stock market index, based on economic news. For this purpose, we have collected 3-year's economic news from January 2015 to December 2017 and adopted Word2Vec model to consider the context of words. To evaluate the result, we performed sentiment analysis to collected news data with the automated constructed lexicon and compared with closings of the KOSPI (Korea Composite Stock Price Index), the South Korean stock market index based on economic news.
Keywords
Stock Prediction; Word2Vec; Natural Language Processing; Text Mining; News; Sentiment Lexicon;
Citations & Related Records
Times Cited By KSCI : 2  (Citation Analysis)
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