# Location reference identification from tweets during emergencies: A deep learning approach

## TL;DR

This paper presents a CNN-based model for extracting location references from tweets during emergencies, achieving high accuracy and enabling improved real-time crisis response and location-based services.

## Contribution

The study introduces a deep learning approach that effectively extracts multi-word location references from noisy tweet text during emergencies, outperforming previous methods.

## Key findings

- Exact matching score of 0.929 for location extraction
- F1-score of 0.96 indicating high accuracy
- Model extracts multi-word location references successfully

## Abstract

Twitter is recently being used during crises to communicate with officials and provide rescue and relief operation in real time. The geographical location information of the event, as well as users, are vitally important in such scenarios. The identification of geographic location is one of the challenging tasks as the location information fields, such as user location and place name of tweets are not reliable. The extraction of location information from tweet text is difficult as it contains a lot of non-standard English, grammatical errors, spelling mistakes, non-standard abbreviations, and so on. This research aims to extract location words used in the tweet using a Convolutional Neural Network (CNN) based model. We achieved the exact matching score of 0.929, Hamming loss of 0.002, and $F_1$-score of 0.96 for the tweets related to the earthquake. Our model was able to extract even three- to four-word long location references which is also evident from the exact matching score of over 92\%. The findings of this paper can help in early event localization, emergency situations, real-time road traffic management, localized advertisement, and in various location-based services.