This research is aimed to solve the tweet/user geolocation prediction task and provide a flexible methodology for the geotagging of textual big data. The suggested approach implements neural networks for natural language processing (NLP) to estimate the location as coordinate pairs (longitude, latitude) and two-dimensional Gaussian Mixture Models (GMMs). The scope of proposed models has been finetuned on a Twitter dataset using pretrained Bidirectional Encoder Representations from Transformers (BERT) as base models. Performance metrics show a median error of fewer than 30 km on a worldwide-level, and fewer than 15 km on the US-level datasets for the models trained and evaluated on text features of tweets' content and metadata context. Our source code and data are available at https://github.com/K4TEL/geo-twitter.git

翻译：本研究旨在解决推文/用户地理位置预测任务，并为文本大数据的空间标注提供一种灵活的方法论。所提出的方法采用自然语言处理（NLP）神经网络，通过坐标对（经度、纬度）和二维高斯混合模型（GMM）进行地理位置估计。研究基于预训练的Transformer双向编码器表征（BERT）作为基础模型，在Twitter数据集上对提出的模型范围进行了微调。性能指标显示，在仅使用推文内容及元数据上下文文本特征进行训练和评估的模型中，全球层面数据集的预测中位误差小于30公里，美国层面数据集的中位误差小于15公里。我们的源代码与数据已公开于https://github.com/K4TEL/geo-twitter.git。