Most existing fault localization approaches utilize statement coverage information to identify suspicious statements potentially responsible for failures. They generally use the binary status information to represent the statement coverage information, indicating a statement executed or not executed. However, the binary information just shows whether a statement is executed or not whereas it cannot evaluate the importance of a statement in a specific execution. Consequently, this may degrade fault localization performance. To address this issue, this study proposes a fault localization approach using term frequency and inverse document frequency. Specifically, the proposed approach constructs an information model to successfully identify the influence of a statement in a test case, and uses the information model to evaluate the suspiciousness of a statement of being faulty. The experiments show that the proposed approach significantly improves fault localization effectiveness.