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@inproceedings{2267006, author = {Tikhonova, Olga and Antonov, Aleksandr and Bogomolov, Jura and Khulbe, Devashish and Sobolevsky, Stanislav}, address = {Amsterdam}, booktitle = {Procedia Computer Science}, doi = {http://dx.doi.org/10.1016/j.procs.2022.10.203}, editor = {Boukhanovsky A., Krzhizhanovskaya V., Klimova A.}, keywords = {urban data analysis; social networks; Natural Language Processing (NLP); Named Entity Recognition; Classification (NERC); Subject-Predicate-Object (SPO) triplets’ extraction}, howpublished = {tištěná verze "print"}, language = {eng}, location = {Amsterdam}, note = {Bude zaindexováno na WoS. Doplnit a poté vykázat do RIV.}, pages = {11-22}, publisher = {Elsevier}, title = {Detecting a citizens' activity profile of an urban territory through natural language processing of social media data}, url = {https://doi.org/10.1016/j.procs.2022.10.203}, year = {2022} }
TY - JOUR ID - 2267006 AU - Tikhonova, Olga - Antonov, Aleksandr - Bogomolov, Jura - Khulbe, Devashish - Sobolevsky, Stanislav PY - 2022 TI - Detecting a citizens' activity profile of an urban territory through natural language processing of social media data PB - Elsevier CY - Amsterdam N1 - Bude zaindexováno na WoS. Doplnit a poté vykázat do RIV. KW - urban data analysis KW - social networks KW - Natural Language Processing (NLP) KW - Named Entity Recognition KW - Classification (NERC) KW - Subject-Predicate-Object (SPO) triplets’ extraction UR - https://doi.org/10.1016/j.procs.2022.10.203 N2 - 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. ER -
TIKHONOVA, Olga, Aleksandr ANTONOV, Jura BOGOMOLOV, Devashish KHULBE and Stanislav SOBOLEVSKY. Detecting a citizens' activity profile of an urban territory through natural language processing of social media data. In Boukhanovsky A., Krzhizhanovskaya V., Klimova A. \textit{Procedia Computer Science}. Amsterdam: Elsevier, 2022, p.~11-22. ISSN~1877-0509. Available from: https://dx.doi.org/10.1016/j.procs.2022.10.203.
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