Publication details
Detecting a citizens' activity profile of an urban territory through natural language processing of social media data
| Authors | |
|---|---|
| Year of publication | 2022 |
| Type | Article in Proceedings |
| Conference | Procedia Computer Science |
| MU Faculty or unit | |
| Citation | |
| Web | https://doi.org/10.1016/j.procs.2022.10.203 |
| Doi | http://dx.doi.org/10.1016/j.procs.2022.10.203 |
| Keywords | urban data analysis; social networks; Natural Language Processing (NLP); Named Entity Recognition; Classification (NERC); Subject-Predicate-Object (SPO) triplets’ extraction |
| Description | The article presents the premises, process, and outcomes of the research, devoted to investigation of the suitability of natural language processing approaches (named entity recognition and subject-predicate-object triplets’ extraction, in particular), applied to social media data, for the problem of building a profile of citizens' activity in an urban territory. Using the named entity recognition approach, supplemented with the custom method of named urban entities distillation, it was possible to build a detailed and representative list of named urban entities for the sample territory of Hatfield, Hertfordshire. Using the subject-predicate-object triplets’ extraction approach, supplemented with the custom activity description patterns, it was possible to get the picture of citizens’ activity corresponding to the identified urban entities. The outcomes were verified on the Twitter and Instagram social networks data and evaluated from the perspectives of the resulting profile quality. |