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Feature selection is often applied to high-dimensional data as a preprocessing step in text classification. When dealing with highly skewed data, we observe that typical feature selection metrics like information gain or chi-squared are biased toward selecting features for the minor class, and the metric of bi-normal separation can select features for both minor and major classes. In this work, we investigate how these feature selection metrics impact on the performance of frequently used classifiers such as decision trees, naive bayes, and support vector machines via bias analysis for highly skewed data. Three types of biases are metric bias, class bias, and classifier bias. Extensive experiments are designed to understand how these biases can be employed in concert and efficiently to achieve good classification performance. We report our findings and present recommended approaches to text classification based on bias analysis and the empirical study.