This paper concerns the creation of a prototype for sentiment analysis, capable of discerning key aspects of an entity under review, and the type of polarity in the response associated with it.
This article focuses on challenges to law enforcement when dealing with massive amounts of data in criminal investigations. To tackle this problem, the financial and fraud investigation unit of a European country has developed a new tool, named LES, that uses Natural Language Processing (NLP) techniques to help criminal investigators handle large amounts of textual information in a more efficient and faster way. In this paper, the authors present briefly this tool and focus on the evaluation its performance in terms of the requirements of forensic investigation: speed, smarter and easier for investigators.
This research will be of interest to the researchers interested in the application of Natural Language Processing across fields, and specifically the lessons that can be learned from Big Data environments. These same techniques may be well applied by law enforcement practitioners in order to filter cyberterrorist information and to improve the investigation of possible leaks.