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.

翻译：新闻可能传达对金融资产的看跌或看涨观点。机构投资者需基于文本数据自动评估隐含的新闻情绪。鉴于每日发布的海量新闻中多数为中性内容，本文提出一种系统性新闻筛选方法，以识别“真正”具有影响力的新闻，旨在更有效地开发新闻情绪学习方法。基于波动率、换手率、买卖价差及订单簿深度等流动性驱动变量，我们将每个5分钟时间区间关联至两种特定流动性模式之一：一种代表市场大部分时间所处的“平静”状态，另一种则以较高波动率和交易量为特征，描述由外部事件驱动的市场状态。随后聚焦于流动性模式从前者切换至后者的时刻，并将附近发布的新闻视为具有影响力。我们以朴素贝叶斯分类器为例，对筛选后的样本进行新闻情绪分类。研究表明，与原始数据集相比，筛选后的数据集能更有效地捕捉特征，从而在短期资产收益预测中取得更优表现。