Public opinion monitoring for proactive crime detection using named entity recognition

Abstract

Public opinion monitoring has been well studied in sociology and informatics. Considerable amounts of crime-related information are available on social media platforms every day. Current methods for monitoring public opinion are typically based on rule matching and manual searching instead of automated processing and analysis. However, the extraction of useful information from large volumes of social media data is a major challenge in public opinion monitoring. This chapter describes a methodology for extracting key information from a large volume of Chinese text using named entity recognition based on the LSTM-CRF model. Since traditional named entity recognition datasets are small and only contain a few types, a custom crime-related corpus was created for training. The results demonstrate that the methodology can automatically extract key attributes such as person, location, organization and crime type with a precision of 87.58%, recall of 83.22% and F1 score of 85.24%.