High Accuracy Location Information Extraction from Social Network Texts Using Natural Language Processing
This work addresses the need for accurate data to predict terrorist attacks, but it is incremental as it builds on existing methods for a specific domain.
The paper tackled the problem of extracting location information from social network texts about terrorism in Burkina Faso, finding that existing NLP solutions had poor accuracy, which their solution resolves.
Terrorism has become a worldwide plague with severe consequences for the development of nations. Besides killing innocent people daily and preventing educational activities from taking place, terrorism is also hindering economic growth. Machine Learning (ML) and Natural Language Processing (NLP) can contribute to fighting terrorism by predicting in real-time future terrorist attacks if accurate data is available. This paper is part of a research project that uses text from social networks to extract necessary information to build an adequate dataset for terrorist attack prediction. We collected a set of 3000 social network texts about terrorism in Burkina Faso and used a subset to experiment with existing NLP solutions. The experiment reveals that existing solutions have poor accuracy for location recognition, which our solution resolves. We will extend the solution to extract dates and action information to achieve the project's goal.