Context Is Key: Addressing Inaccurate Geolocation in Text Data
Automated text processing often struggles with inaccurate geolocation mentions. This article introduces a two-stage supervised machine-learning algorithm designed to tackle this issue.
Experimental Approach
We extract contextual information from texts, including N-gram patterns for location words, mention frequency, and sentence context containing these words. Using training data sets, we estimate model parameters before applying it to test data sets.
Key Findings & Implications
Our results demonstrate that the algorithm significantly outperforms existing geocoders, even when handling additional zeros in a post-hoc case study. This advancement enhances event localization accuracy using news articles from around the world.
