Predicting the Geolocation of Tweets Using transformer models on Customized Data
Fuente:
arXiv
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| Autori principali: | , |
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| Natura: | Preprint |
| Pubblicazione: |
2023
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| _version_ | 1866916559401779200 |
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| author | Lutsai, Kateryna Lampert, Christoph H. |
| author_facet | Lutsai, Kateryna Lampert, Christoph H. |
| contents | 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 |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2303_07865 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Predicting the Geolocation of Tweets Using transformer models on Customized Data Lutsai, Kateryna Lampert, Christoph H. Computation and Language Artificial Intelligence Information Retrieval Machine Learning 68T50 I.2.7 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 |
| title | Predicting the Geolocation of Tweets Using transformer models on Customized Data |
| topic | Computation and Language Artificial Intelligence Information Retrieval Machine Learning 68T50 I.2.7 |
| url | https://arxiv.org/abs/2303.07865 |