High Accuracy Location Information Extraction from Social Network Texts Using Natural Language Processing

TL;DR

This paper presents a novel NLP-based method for accurately extracting location information from social network texts related to terrorism, addressing the poor performance of existing solutions.

Contribution

The paper introduces an improved NLP approach that significantly enhances location recognition accuracy in social media texts about terrorism.

Findings

Existing NLP solutions have poor location recognition accuracy.

The proposed method improves location extraction performance.

The dataset of 3000 social network texts supports terrorism-related analysis.

Abstract

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.