This paper proposed a method to find annotations corresponding to given CNN news documents for detecting terrorism image or context information. Assigning keywords or annotation to image is one of the important tasks to let machine understand web data written by human. Many techniques have been suggested for automatic image annotation in the last few years. Many researches focused on the method to extract possible annotation using low-level image features. This was the basic and traditional approach but it has a limitation that it costs lots of time. To overcome this problem, we analyze images and theirs co-occurring text data to generate possible annotations. The text data in the news documents describe the core point of news stories according to the given images and titles. Because of this fact, this paper applied text data as a resource to assign image annotations using TF (Term Frequency) value and WUP values of WordNet. The proposed method shows that text analysis is another possible technique to annotate image automatically for detecting unintended web documents.