Abstract
- Textual social media content and short messages have gained in importance as evidence in criminal investigations. Yet, the large number of textual data poses a great challenge for investigators. Even though text retrieval systems can assist in finding evidential messages, the success of the search still depends on entering appropriate search terms. However, for colloquial texts these are difficult to determine because one cannot be sure about what terms are used in the texts and might be of interest. Therefore, the aim is to develop a method that recommends keywords and searches phrases based on the underlying data. A particular challenge here is that the appropriate search terms are often non-obvious words that are not expected to be found in the data, but are particularly relevant. In total, four methods were evaluated for extracting and suggesting the most relevant terms and phrases using a real-life dataset. The best results were obtained with topic modeling considering syntagmatic relations.