  News can convey bearish or bullish views on financial assets. Institutional
investors need to evaluate automatically the implied news sentiment based on
textual data. Given the huge amount of news articles published each day, most
of which are neutral, we present a systematic news screening method to identify
the ``true'' impactful ones, aiming for more effective development of news
sentiment learning methods. Based on several liquidity-driven variables,
including volatility, turnover, bid-ask spread, and book size, we associate
each 5-min time bin to one of two specific liquidity modes. One represents the
``calm'' state at which the market stays for most of the time and the other,
featured with relatively higher levels of volatility and trading volume,
describes the regime driven by some exogenous events. Then we focus on the
moments where the liquidity mode switches from the former to the latter and
consider the news articles published nearby impactful. We apply naive Bayes on
these filtered samples for news sentiment classification as an illustrative
example. We show that the screened dataset leads to more effective feature
capturing and thus superior performance on short-term asset return prediction
compared to the original dataset.
