Stock market volatility is a widely and deeply studied subject across academic and industrial settings. The increase in internet data usage, collection, and availability has also led to studies examining the links between internet search history and the stock market. In this paper we attempt to predict future stock market volatility, otherwise known as realized volatility, in Korean equity markets using Naver search trend data in a Long Short-Term Memory (LSTM) machine learning model. We find that LSTM networks showed an improvement in predicting realized volatility over the traditional GARCH model when compared using the mean average percentage error (MAPE) metric in both the KOSPI and KOSDAQ indices. We also find that we can improve on our original LSTM network with feature selection and expansion of the observation interval and normalization window.